Dissertations / Theses on the topic 'Medical Machines'
To see the other types of publications on this topic, follow the link: Medical Machines.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Medical Machines.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Tjora, Aksel Hagen. "Caring machines : Emerging practices of work and coordination in the use of medical emergency communication technology." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Social Sciences and Technology Management, 1997. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13.
Full textAbstract:
Stadig mer forskning fokuserer på utviklingen og bruken av teknologi, ikke minst i forbindelse med den stadige mer utbredte bruken av informasjons- og kommunikasjonsteknologi. Mange av disse studiene har vært motivert av ønsket om å vise til de fantastiske mulighetene som organisasjoner (særlig bedrifter) har ved å nyttiggjøre seg nyvinningene (se f.eks. Davidow og Malone, 1992 og Scott Morton, 1991). Mange samfunnsvitenskapelige studier har imidlertid inntatt en mye mer kritisk holdning til de teknologiske nyvinningene. Innenfor sosiologien er det flere slike tilnærminger.
Sosiologiske perspektiver på teknologi
I de funksjonalistiske tilnærmingene fokuseres det på hvilke effekter de tekniske systemene har på brukerne av dem, og spesielt hvordan alle systemer medfører uintenderte konsekvenser, blant annet ved at de nye systemenes latente funksjoner (Merton, 1967) trer fram i dagen etterhvert som systemene kommer i bruk. I disse studiene betrakter man de tekniske systemene som makrostrukturer som følger sin egen utvikling mer eller mindre uavhengig av brukerne (dvs de er teknologideterministiske).
I Marxistiske tilnærminger unngår man en ensidig determinisme ved at teknologiene antas å være i dialektisk motsetning til de sosiale systemene. Spesielt betraktes teknologiske nyvinninger som kapitalistenes middel for å beholde sitt herredømme over arbeiderklassen. I nyere perspektiver (se f.eks. Winner, 1977; 1986, Hirschorn, 1984; Feenberg, 1991) påpeker man at det er de kulturelle verdiene som er knyttet til teknologidesign som medfører uheldige konsekvenser (som for eksempel degradering av arbeidskraft), og ikke teknologien i seg selv.
Tilsvarende fokuserer de sosialkonstruktivistiske studiene (Bijker, Hughes og Pinch, 1987; Bijker og Law, 1992; Law, 1991) på hvordan den teknologiske utviklingen eller de teknologiske nnovasjonene ikke følger naturlige utviklingsveier, men konstrueres i nettverk av aktører som hver på sin måte presser fram sine interesser i forhold til et teknologisk artefakt. Mange av konstruktivistene benekter et skille mellom tekniske og sosiale systemer (eller aktører). De mener at det er umulig å egentlig separere det tekniske og sosiale, og velger i stedet å betrakte de totale relasjonene som et sømløst vev. Konstruktivistene bruker spesielt historiske studier av teknologi-utvikling for å identifisere aktører i slike vev, og dermed undersøke hva som ligger bak de løsninger som velges i utviklingen av tekniske artefakter.
I de senere årene er det blitt flere forskere som ved å bruke etnografiske studier av teknologisk praksis undersøker hvordan tekniske og sosiale aktører samhandler. I disse studiene er man i motsetning til de konstruktivistiske tilnærmingene mer opptatt av bruken av teknologi enn utviklingen av den. Men i samme ånd som konstruktivistene er man opptatt av å vise hvordan den teknologiske praksis i sterk grad utvikles ved hjelp av sosiale mekanismer, for eksempel i arbeidsgrupper, og hvordan tekniske praksisimperativer rekonstrueres i daglig sosial praksis (se f.eks. Suchman, 1987; Hutchins, 1988; 1990; 1995; Hutchins og Klausen, 1996; Heath og Luff, 1992; 1996; Orr, 1996; Engeström og Middleton, 1996).
Alle disse tilnærmingene har viktige bidrag til sosiologiske studier av utvikling og bruk av teknologi. Imidlertid ser det ut til at det er vanskelig å skape en teoretisk syntese av teorier som bygger på såpass forskjellige antakelser. I denne avhandlingen kombinerer jeg imidlertid deler fra teoriene ved et feltstudium der én type teknologi benyttes i flere ulike kontekster, slik at både aktør-perspektiver og struktur-perspektiver blir relevante. Et empirisk felt som gir denne muligheten er bruken av medisinske nødmeldesentraler i Norge.
The study of technology has recently become more focused in various schools of sociology. However, Marxist, functionalist, social constructivist, and ethnographic research, have tended to explain technological development either from macro or micro perspectives. Further research is needed to increase our understanding of technology as situated in its social and institutional contexts, where individual and professional relations are considered. In this thesis, elements from several approaches are applied to the study of communication technology in Norwegian medical emergency communication centres.
About ten years ago, LV (doctor-on-call) centres, each manned by one nurse to handle local requests for a doctor, were established in nursing homes. AMK (acute medical communication) centres were introduced in hospitals, and are manned by teams of two to four nurses and ambulance coordinators to handle medical emergency calls (113), internal hospital alarms and local requests for a doctor. Even though the intensity and work loads are very different between the LV and AMK centres, the technical artefacts that are used are basically similar in both types of centre.
Using a comparative case approach, the use of technology was studied through interviews with nurses, doctors and administrative personnel and by observations of the work in six LV and three AMK centres.
There are three main findings in this thesis. First, the operation of LV centres in nursing homes conflicts with the general nursing home practice, and many LV centres are redefined by its users as switchboards to decrease the burden that is placed upon them.
Second, the nurses who work with requests for doctors in a similar way in the AMK centres in fact manage to solve many problems on the phone. The thesis discusses how these differences have emerged from performing the same job with the same technological tools.
Third, the handling of emergency calls at the AMK centres is accomplished through intense social and technically coordinated work. An ideal model of this kind of coordination, “the coordinated climate”, is developed from the observations in the AMK centres, and results from control room studies are applied.
The three findings are summarised in a discussion of how structures constrain and facilitate social and technological practice.
2
Yao, Jing M. Eng Massachusetts Institute of Technology. "Reduce cycle time and work in process in a medical device factory : scheduling of needle hub molding machines." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42326.
Full textAbstract:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (p. 51).
Many manufacturing firms have improved their operations by implementing a work-in-process (WIP) limiting control strategy. This project explores the application of this concept to limit WIP and reduce cycle time for the Becton, Dickinson and Company's manufacturing facility in Tuas, Singapore. BD's Eclipse Safety Needle production line is facing increasing pressure to reduce its high WIP and long cycle times. With the forecast of increasing demand, the current production control practice will sooner or later push the shop floor space to a limit. We divided the overall system into three manageable sub-systems and analyzed different strategies for each. This paper documents the approaches to schedule 30 molding machines. These machines are located at the first stage of the production line. Although the total production rate of the 30 machines is higher than the downstream machines, the production rate of each product type is much slower because of machine constraints. This project groups the 30 machines into three groups, and proposes different strategies to reduce the total WIP level and cycle time.
by Jing Yao.
M.Eng.
3
Taylor, Ashley Rae. "Innovating for Global Health through Community-Based Participatory Research: Design of Mechanical Suction Machines for Rural Health Clinics in Malawi." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72975.
Full textAbstract:
Clinicians in low and middle-income countries (LMIC) face many challenges, including high patient-to-staff ratios, limited resources, and inconsistent access to electricity. This research aimed to improve health outcomes in LMIC through an enlightened understanding of challenges associated with healthcare technology. To understand LMIC barriers to acquiring, maintaining, and repairing medical equipment, a community-based participatory study was conducted at three clinical settings in southern Malawi. Thirty-six clinical staff participated in surveys and focus groups to provide information on medical device challenges. Results from the study emphasize the importance of community-based participatory innovation to improve global health. Many clinical staff expressed frustration regarding inability to prevent patient mortality attributed to equipment failure.
Data from the community-based participatory study of medical technology conducted in Malawi revealed key insights for designing for low and middle income countries, and more specifically, for communities in southern Malawi. Specifically, partner communities identified mechanical suction machines as a top priority for design innovation. Working with technical and clinical staff in Malawian communities, a prototype mechanical suction machine was designed and constructed.
This work suggests that engineers working in low and middle income countries face a unique sundry of design requirements that require an intimate understanding of the local community, including community leaders, community beliefs and values, and locally available resources. Technology innovation for global health should incorporate community expertise and assets, and health and technical education efforts should be developed to increase working knowledge of medical devices.Master of Science
4
Osman, Mohamud Maria, and Ubilla Fernanda Sanchez. "Ultraljudsutbildningar för medicintekniska ingenjörer : Behovsinventering, inköpsprocedurer och effekter." Thesis, KTH, Medicinteknik och hälsosystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298194.
Full textAbstract:
Denna studie syftar till att undersöka hur ultraljudsutbildningar för ingenjörer köps in, vad de innehåller, hur de genomförs, utvärderas och vilka resultatutbildningarna leder till. Totalt intervjuades sex sjukhus runt om i Sverige där elva medicintekniska ingenjörer och fyra verksamhetschefer deltog. En kvalitativ metod användes i studien i form av semistrukturerade intervjuer som grund för att analysera frågeställningarna. Resultatet visade att utbildningar köps in i upphandling av nya ultraljudsmaskiner och genomförs under garantiåren. Utbildningarna hålls av leverantörerna och de brukar vara i två dagar. Effekterna av utbildningarna varierar och beror på vilket serviceavtal som sjukhusen har. Det saknas en formell modell för utvärdering och uppföljning, trots det faktum att det i årliga möten diskuteras hur utbildningarna har gått och vilka kompetenser som behövs. Resultatet från studien kan främst användas i syfte att skapa bättre utbildningar, underlätta kommunikationen mellan sjukhus och leverantör om vad kursen innebär, samt vad ingenjörerna föredrar för innehåll i kurser för att kunna utvecklas inom ultraljud.This study aims to investigate how ultrasound training for engineers is purchased, including how it is carried out and evaluated, what the different courses contain and what result the courses lead to. Six hospitals around Sweden were interviewed, where eleven medical engineers and four business managers participated. A qualitative method was used in the study with semi-structured interviews as a basis for analysing the issues. The results showed that the training courses are purchased in the procurement of new ultrasound machines and are carried out during the warranty years. The suppliers hold the training courses, which are usually held for two days. The effects of training vary and depend on the service agreement that the hospitals have. There is no formal model for evaluation and follow-up, even though annual meetings discuss how the training has gone and what skills are needed. The results can mainly be used to create better training and improve communication between hospital and supplier about what the course entails and what the engineers prefer for the content of courses to develop in the area.
5
Boaretto, Neury. "Classificação de defeitos de soldagem em imagens radiográficas PDVD de tubulações de petróleo: uma abordagem com ensemble de Extreme Learning Machines." Universidade Tecnológica Federal do Paraná, 2014. http://repositorio.utfpr.edu.br/jspui/handle/1/2890.
Full textAbstract:
A inspeção de defeitos de soldagem em imagens radiográficas de tubulações é bastante subjetiva e está sujeita a erros de interpretação por parte do inspetor laudista. Dentro desse contexto, nos últimos anos tem-se visto um grande esforço no desenvolvimento de métodos automáticos e semiautomáticos de detecção de defeitos em juntas soldadas. Este trabalho apresenta um método automatizado para detecção e classificação de defeitos em imagens radiográficas de juntas soldadas de tubulações obtidas pela técnica de exposição radiográfica parede dupla vista dupla (PDVD), obtidas em reais situações de campo e que, geralmente, têm uma qualidade mais baixa do que as imagens usadas em outros estudos. O método proposto identifica na imagem a região do cordão de solda, detecta as descontinuidades e classifica as mesmas em defeitos e não defeitos, destacando na imagem o resultado. São avaliados classificadores a partir de métodos de classificação por redes neurais Multilayer Perceptron (MLP), redes neurais Extreme Learning Machines (ELM) e classificador estatístico Support Vector Machines (SVM). O método proposto para identificação da região de interesse atingiu 100% de precisão na segmentação do cordão de solda. O classificador SVM apresentou um desempenho melhor que os classificadores MLP e ELM em todos os cenários testados. Com a utilização de ensembles de ELMs obteve-se um F-score de 85,7% para o banco de padrões de teste, resultados satisfatórios quando comprados com trabalhos semelhantes. O uso de ensembles de ELMs representa um ganho de apenas 0,5% no F-score em comparação com o melhor resultado de rede treinada individualmente, entretanto, com o uso de faixas de limiares de decisão do ensemble, o uso do método permite mostrar as descontinuidades sobre as quais o ensemble não tem certeza, destacando na imagem estas descontinuidades. A imagem resultate da aplicação do método serve como auxílio ao especialista na elaboração de laudos.The inspection of radiographic images of welded joints is very subjective and is subject to errors of interpretation by the inspector. In this context, a great effort has been made in the last years to develop automatic and semiautomatic methods for detecting defects in welded joints. This research work presents an automated method for the detection and classification of defects in radiographic images of welded joints of pipes obtained by the double wall double image (DWDI) exposure technique obtained in real field situations and which generally have a lower quality than the images used in other studies. The proposed methos identifies the region of the weld bead, detects the discontinuities and classifies them as defects and non-defects, highlighting in the image the result. Classifiers are evalueted using methods of classification by multilayer perceptron (MLP) neural networks, extreme learning machines (ELM) neural networks, and Support Vector Machines (SVM). The proposed method for identifying the region of interest reached 100% precision in the segmentation od the weld bead. The SVM classifier performed better than the MLP and ELM classifiers in all scenarios tested. Using ELM ensembles, an F_score of 85,7% was obtained for a test patterns database, satisfactoryresults when compared to similar works. The use of ensembles of ELMs represents a gain of only 0,5% in the F-score compared to the best result of the individually trained network, however, with the use of ensemble decision threshold ranges, the presented method allows to show the discontinuities about which the ensemble is not sure, highlighting in the image these discontinuities as a region of uncertainty, leaving to the specialist the final evaluation of these discontinuities. The image resulting from the application of the method serves as an aid to the expert in the elaboration of reports.
6
Veropoulos, Konstantinos. "Machine learning approaches to medical decision making." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367661.
Full text
7
Smyth, Katherine Marie. "Piezoelectric micro-machined ultrasonic transducers for medical imaging." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108938.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 175-184).
Next generation medical imaging technology hinges on the development of cost effective and array compatible transducers making piezoelectric micro-machined ultrasonic transducers (pMUTs) an attractive alternative to the current bulk piezoelectric technology. This thesis aims to realize pMUT potential starting with the development of an effective single cell model that is further scaled to optimize multi-cell elements in a 1D array. In the first half of this work, a transverse mode, lead zirconate titanate (PZT) pMUT plate cell is fabricated using common micro-fabrication techniques and a PZT sol-gel deposition process. Through derivation using a novel Greens function solution technique, an equivalent circuit model with explicitly defined lumped parameters is presented and validated through electrical impedance measurements of fabricated devices and finite element modeling. The equivalent circuit is a crucial design tool as transducer performance metrics, including experimentally validated acoustic domain values, are shown to be defined directly from the lumped parameters. In the second half, figures of merit are identified from these performance metrics and an expanded multi-cell model is employed to strategically target improvements in both bandwidth and coupling while maintaining high pressure output. The resulting, optimized multicell elements in a 1D array are fabricated via a commercially viable, wafer-scale manufacturing process including a novel PZT dry etch. A top-down fabrication approach facilitates achievement of the largest active area of a multi-cell pMUT to date consisting of over 1000 cells in a 200pm x 4mm element footprint, and more substantially, results in the highest electromechanical coupling recorded for a pMUT to date measured at 9 ± 1.4% per element.
by Katherine Marie Smyth.
Ph. D.
8
Chi, Chih-Lin Street William N. "Medical decision support systems based on machine learning." Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/283.
Full text
9
Chi, Chih-Lin. "Medical decision support systems based on machine learning." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/283.
Full textAbstract:
This dissertation discusses three problems from different areas of medical research and their machine learning solutions. Each solution is a distinct type of decision support system. They show three common properties: personalized healthcare decision support, reduction of the use of medical resources, and improvement of outcomes.
The first decision support system assists individual hospital selection. This system can help a user make the best decision in terms of the combination of mortality, complication, and travel distance. Both machine learning and optimization techniques are utilized in this type of decision support system. Machine learning methods, such as Support Vector Machines, learn a decision function. Next, the function is transformed into an objective function and then optimization methods are used to find the values of decision variables to reach the desired outcome with the most confidence.
The second decision support system assists diagnostic decisions in a sequential decision-making setting by finding the most promising tests and suggesting a diagnosis. The system can speed up the diagnostic process, reduce overuse of medical tests, save costs, and improve the accuracy of diagnosis. In this study, the system finds the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process.
The third decision support system recommends the best lifestyle changes for an individual to lower the risk of cardiovascular disease (CVD). As in the hospital recommendation system, machine learning and optimization are combined to capture the relationship between lifestyle and CVD, and then generate recommendations based on individual factors including preference and physical condition. The results demonstrate several recommendation strategies: a whole plan of lifestyle changes, a package of n lifestyle changes, and the compensatory plan (the plan that compensates for unwanted lifestyle changes or real-world limitations).
10
Letzner, Josefine. "Analysis of Emergency Medical Transport Datasets using Machine Learning." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215162.
Full textAbstract:
The selection of hospital once an ambulance has picked up its patient is today decided by the ambulance staff. This report describes a supervised machinelearning approach for predicting hospital selection. This is a multi-classclassification problem. The performance of random forest, logistic regression and neural network were compared to each other and to a baseline, namely the one rule-algorithm. The algorithms were applied to real world data from SOS-alarm, the company that operate Sweden’s emergency call services. Performance was measured with accuracy and f1-score. Random Forest got the best result followed by neural network. Logistic regression exhibited slightly inferior results but still performed far better than the baseline. The results point toward machine learning being a suitable method for learning the problem of hospital selection.Beslutet om till vilket sjukhus en ambulans ska köra patienten till bestäms idag av ambulanspersonalen. Den här rapporten beskriver användandet av övervakad maskininlärning för att förutsåga detta beslut. Resultaten från algoritmerna slumpmässig skog, logistisk regression och neurala nätvärk jämförs med varanda och mot ett basvärde. Basvärdet erhölls med algorithmen en-regel. Algoritmerna applicerades på verklig data från SOS-alarm, Sveriges operatör för larmsamtal. Resultaten mättes med noggrannhet och f1-poäng. Slumpmässigskog visade bäst resultat följt av neurala nätverk. Logistisk regression uppvisade något sämre resultat men var fortfarande betydligt bättre än basvärdet. Resultaten pekar mot att det är lämpligt att använda maskininlärning för att lära sig att ta beslut om val av sjukhus.
11
Rosén, Henrik. "Automation of Medical Underwriting by Appliance of Machine Learning." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171843.
Full textAbstract:
One of the most important fields regarding growth and development for mostorganizations today is the digitalization, or digital transformation. The offering oftechnological solutions to enhance existing, or create new, processes or products isemerging. That is, it’s of great importance that organizations continuously affirm thepotential of applying new technical solutions into their existing processes. For example, a well implemented AI solution for automation of an existing process is likely tocontribute with considerable business value.Medical underwriting for individual insurances, which is the process consideredin this project, is all about risk assessment based on the individuals medical record.Such task appears well suited for automation by a machine learning based applicationand would thereby contribute with substantial business value. However, to make aproper replacement of a manual decision making process, no important informationmight be excluded, which becomes rather challenging due to the fact that a considerable fraction of the information the medical records consists of unstructured textdata. In addition, the underwriting process is extremely sensible to mistakes regarding unnecessarily approve insurances where an enhanced risk of future claims can beassessed.Three algorithms, Logistic Regression, XGBoost and a Deep Learning model, wereevaluated on training data consisting of the medical records structured data from categorical and numerical answers, the text data as TF-IDF observation vectors, and acombination of both subsets of features. The XGBoost were the classifier performingbest according to the key metric, a pAUC over an FPR from 0 to 0.03.There is no question about the substantial importance of not to disregard anytype of information from the medical records when developing machine learning classifiers to predict the medical underwriting outcomes. At a very risk conservative andperformance pessimistic approach the best performing classifier did manage, if consider only the group of youngest kids (50% of sample), to recall close to 50% of allstandard risk applications at a false positive rate of 2%, when both structured andtext data were considered. Even though the structured data accounts for most of theexplanatory ability it becomes clear that the inclusive of the text data as TF-IDF observation vectors make for the differences needed to potentially generate a positivenet present value to an implementation of the model
12
Punugu, Venkatapavani Pallavi. "Machine Learning in Neuroimaging." Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10284048.
Full textAbstract:
The application of machine learning algorithms to analyze and determine disease related patterns in neuroimaging has emerged to be of extreme interest in Computer-Aided Diagnosis (CAD). This study is a small step towards categorizing Alzheimer's disease, Neurode-generative diseases, Psychiatric diseases and Cerebrovascular Small Vessel diseases using CAD. In this study, the SPECT neuroimages are pre-processed using powerful data reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA) and Automated Anatomical Labeling (AAL). Each of the pre-processing methods is used in three machine learning algorithms namely: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and k-Nearest Neighbors (k-nn) to recognize disease patterns and classify the diseases. While neurodegenerative diseases and psychiatric diseases overlap with a mix of diseases and resulted in fairly moderate classification, the classification between Alzheimer's disease and Cerebrovascular Small Vessel diseases yielded good results with an accuracy of up to 73.7%.
13
Gurudath, Nikita. "Diabetic Retinopathy Classification Using Gray Level Textural Contrast and Blood Vessel Edge Profile Map." Ohio University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1417538885.
Full text
14
Fernandez, Sanchez Javier. "Knowledge Discovery and Data Mining Using Demographic and Clinical Data to Diagnose Heart Disease." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233978.
Full textAbstract:
Cardiovascular disease (CVD) is the leading cause of morbidity, mortality, premature death and reduced quality of life for the citizens of the EU. It has been reported that CVD represents a major economic load on health care sys- tems in terms of hospitalizations, rehabilitation services, physician visits and medication. Data Mining techniques with clinical data has become an interesting tool to prevent, diagnose or treat CVD. In this thesis, Knowledge Dis- covery and Data Mining (KDD) was employed to analyse clinical and demographic data, which could be used to diagnose coronary artery disease (CAD). The exploratory data analysis (EDA) showed that female patients at an el- derly age with a higher level of cholesterol, maximum achieved heart rate and ST-depression are more prone to be diagnosed with heart disease. Furthermore, patients with atypical angina are more likely to be at an elderly age with a slightly higher level of cholesterol and maximum achieved heart rate than asymptotic chest pain patients. More- over, patients with exercise induced angina contained lower values of maximum achieved heart rate than those who do not experience it. We could verify that patients who experience exercise induced angina and asymptomatic chest pain are more likely to be diagnosed with heart disease. On the other hand, Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Tree, Bagging and Boosting methods were evaluated by adopting a stratified 10 fold cross-validation approach. The learning models provided an average of 78-83% F-score and a mean AUC of 85-88%. Among all the models, the highest score is given by Radial Basis Function Kernel Support Vector Machines (RBF-SVM), achieving 82.5% ± 4.7% of F-score and an AUC of 87.6% ± 5.8%. Our research con- firmed that data mining techniques can support physicians in their interpretations of heart disease diagnosis in addition to clinical and demographic characteristics of patients.
15
Postovskaya, Anna. "Rule-based machine learning for prediction of Macaca mulatta SIV-vaccination outcome using transcriptome profiles." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-440182.
Full textAbstract:
One of the reasons, why the development of an effective HIV vaccine remains challenging, is the lack of understanding of potential vaccination-induced protection mechanisms. In the present study, Rhesus Macaques (Macaca mulatta) gene expression profiles obtained during vaccination with promising candidate vaccines against Simian Immunodeficiency Virus (SIV) were processed with a rule-based supervised machine learning approach to analyze the effects of vaccine combination treatment. The findings from constructed rule-based classifiers suggest that the immune response against SIV builds up throughout the immunization procedure. The upregulation of three genes (NHEJ1, GBP7, LAMB1), known to contribute to immune system development and functioning, cellular signalling, and DNA reparation, during or after vaccination boost appears to play an important role in the development of protection against SIV. What is more, the data suggest that the mechanisms of protection development might be dependent on the vaccine type providing a plausible explanation for the difference in effect between vaccines. Further studies are necessary to confirm or disprove our preliminary understanding of the vaccination-induced protection mechanisms against SIV and to use this information for rational vaccine design.
16
Aldosaky, Khatoon Salim Eshaq. "MANUAL VS MACHINERY SMALL RNA EXTRACTION BY USING A QIACUBE® MACHINE : Two methods. Two volumes." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18854.
Full textAbstract:
Sepsis is a serious condition caused by a dysregulated immune response of the host triggered by an infection that can potentially lead to malfunction of various organs or even death in severe cases. Some studies have shown that the use of biomarkers could aid in early diagnosis as well as early treatment of sepsis patients. Furthermore, various studies have investigated the idea of using extracellular microRNAs as biomarkers for sepsis diagnosis. This study aimed to see if there were any differences in the quantity and purity of small RNA -which includes microRNA- by performing two different RNA extraction methods (manual and machinery by using a QIAcube) as well as two different volumes by using the ExoRNeasy Serum/Plasma Midi Kit. Blood samples were collected solely from the same self-assessed healthy donor. The plasma samples were frozen and then thawed before the RNA extraction, whether manually or machinery by the QIAcube. The extracted small RNA was then measured for quantity and purity. The quantitative results were analysed by ANOVA followed by post-hoc Tukey test to show the statistically significant difference in the concentration of small RNA. The QIAcube showed higher concentration values compared to the manual method as well as larger initial plasma volume in comparison to the lower initial plasma volume. Meanwhile, the Kruskal-Wallis test showed no statistically significant difference in the purity values among the different methods and volumes. In conclusion, based on this study, the QIAcube could do what human hands do.
17
Strid, Tobias. "The enzymatic machinery of leukotriene biosynthesis : Studies on ontogenic expression, interactions and function." Doctoral thesis, Linköpings universitet, Cellbiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-74785.
Full textAbstract:
Leukotrienes (LTs) are biologically active arachidonic acid (AA) derivatives generated by the 5-lipoxygenase (5-LO) pathway. They are produced by myeloid cells. 5-LO converts AA to LTA4 in cooperation with 5-LO activating protein (FLAP). LTA4 is converted to LTB4, by LTA4-hydrolase (LTA4H) or to LTC4 by LTC4-synthase (LTC4S). LTs act on cells through plasma membrane bound G-protein coupled receptors found on leukocytes, smooth muscle and endothelial cells. We report here protein-protein interactions of proteins involved in LTC4 synthesis. 5-LO interacts with cytosolic domains of the integral membrane proteins FLAP and LTC4S at the nuclear envelope, in addition LTC4S interacts with FLAP through its hydrophobic membrane spanning regions. We constructed an LTC4S promoter controlled GFP reporter vector, displaying cell specific expression and sensitivity to agents known to affect LTC4S expression. The vector was used to create transgenic mice expressing GFP as a reporter for LTC4S. Ontogenic mouse expression studies revealed that the complete LT biosynthesis machinery was present at e11.5 primarily in the hematopoietic cells colonizing the liver. Although mature myeloid cells were the main contributors, a substantial amount of FLAP message was also detected in hematopoietic stem and progenitor cells, indicating possible functions for FLAP in hematopoietic regulation. Functional analyses using FLAP knockout mice suggested fine-tuning roles for LTs during differentiation, primarily along the B-lymphocyte differentiation path.
18
Folk, Lillian C. "A study of the Veterinary Medical Database /." Free to MU Campus, others may purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1421133.
Full text
19
Hjalmarsson, Victoria. "Machine learning and Multi-criteria decision analysis in healthcare : A comparison of machine learning algorithms for medical diagnosis." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33940.
Full textAbstract:
Medical records consist of a lot of data. Nevertheless, in today’s digitized society it is difficult for humans to convert data into information and recognize hidden patterns. Effective decision support tools can assist medical staff to reveal important information hidden in the vast amount of data and support their medical decisions. The objective of this thesis is to compare five machine learning algorithms for clinical diagnosis. The selected machine learning algorithms are C4.5, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Naïve Bayes classifier. First, the machine learning algorithms are applied on three publicly available datasets. Next, the Analytic hierarchy process (AHP) is applied to evaluate which algorithms are more suitable than others for medical diagnosis. Evaluation criteria are chosen with respect to typical clinical criteria and were narrowed down to five; sensitivity, specificity, positive predicted value, negative predicted value and interpretability. Given the results, Naïve Bayes and SVM are given the highest AHP-scores indicating they are more suitable than the other tested algorithm as clinical decision support. In most cases kNN performed the worst and also received the lowest AHP-score which makes it the least suitable algorithm as support for medical diagnosis.
20
Bradley, Andrew Peter. "Machine learning for medical diagnostics: Techniques for feature extraction, classification, and evaluation." Thesis, University of Queensland, 1996.
Find full textAbstract:
The use of computers as diagnostic aids in medicine is becoming a reality in the clinical arena; a major factor to this trend being the successful application of machine learning techniques. Three fundamentally different approaches to machine learning have been identified, which we call Exemplar, Hyper-plane, and Hyper-rectangle based methods. Part of this thesis is devoted to a novel hyper- rectangle based algorithm called the Multiscale Classifier (MSC), which is implemented as an inductive decision tree. The MSC can be applied to any N-dimensional classification problem, successively splitting feature space in half, using logic minimisation to control tree growth. Pruning techniques are then used to produce decision trees that are sensitive to the misclassification cost of examples. Such techniques are shown to produce different operational modes of classification which may be visualised using the Receiver Operating Characteristic (ROC) curve. The MSC has several significant advantages over other existing hyper-rectangle based approaches: learning is incremental; the tree is non-binary; and backtracking of decisions is possible.
A feature extraction technique based on scale-space analysis is proposed and applied to texture measures extracted from images of cervical cell nuclei. Specifically, we model, as a function of scale, features derived from a Grey Level Co-occurrence Matrix (GLCM). On this data set the proposed technique was found to offer an improvement in performance over conventional feature extraction techniques.
Methodologies for the evaluation of a number of machine learning algorithms (Bayesian, C4.5, K-NN, Perceptron, Multi-layer Perceptron, and the MSC) are explored using six "real world" medical diagnostic data sets. The performance of each algorithm is evaluated in terms of overall accuracy, sensitivity, specificity, area under the ROC curve (AUC), X2 test statistic, training time, and interpret ability. For each data set, an Analysis of Variance (ANOVA) is used to test the statistical significance of any differences between the cross-validated estimates of the accuracy and AUC performance measures. The benefits of AUC over accuracy as a performance measure are discussed in terms of increased statistical sensitivity, independence from a decision threshold, and invariance to prior class probabilities. It was found that the exemplar and hyper-plane based methods had marginally higher accuracies when compared to the hyper-rectangle based methods. However, the hyper-rectangle based methods are often more interpretable and less computationally intensive. The MSC was found to compare favourably with the other learning algorithms and has been established as a useful additional tool for machine learning in medical diagnostics.
21
Bates, Russell. "Learning to extract tumour vasculature : techniques in machine learning for medical image analysis." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:933383a8-be39-44df-9beb-af94b32723ab.
Full textAbstract:
Cancer is a leading cause of death worldwide with an estimated 14 million new cases occurring yearly and approximately 8 million deaths. Although much progress has been made in the understanding and treatment of cancer, there are still many mechanisms that remain poorly understood. The development of vasculature is known to be a key element in facilitating the growth of a tumour. Modern imaging modalities such as multi-photon fluorescence microscopy allow unprecedented opportunities to examine and quantify this vasculature in vivo. However, the appearance of vascular networks, imaged at these scales, can be extremely complex and the automatic delineation of such large, tortuous and chaotic vascular networks is a non-trivial task. In this thesis we develop a number of methods for the automatic delineation of tumour vasculature, imaged using in vivo microscopy. Recent developments in machine learning have provided a powerful set of techniques for the automated analysis of complex structures in images. Leveraging these, it is possible to develop algorithms, capable of learning from human annotations, which are able to analyse extremely large images quickly and with a high degree of accuracy. The key contributions of this thesis are as follows: we present a novel supervoxel algorithm for use in a lightweight machine learning framework for segmentation. We adapt the current state-of-the-art in segmentation using 2D deep fully convolutional neural networks for use in 3D vascular segmentation. We further demonstrate the use of hybrid convolutional-recurrent networks for extracting 3D vessel centrelines. We propose the use of Conditional Adversarial Networks for refining the extraction of vessel centrelines directly. Finally, we demonstrate the ability of the developed methods to make quantitative observations on longitudinal changes to in vivo tumour vasculature development.
22
Frunza, Oana Magdalena. "Personalized Medicine through Automatic Extraction of Information from Medical Texts." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22724.
Full textAbstract:
The wealth of medical-related information available today gives rise to a multidimensional source of knowledge. Research discoveries published in prestigious venues, electronic-health records data, discharge summaries, clinical notes, etc., all represent important medical information that can assist in the medical decision-making process. The challenge that comes with accessing and using such vast and diverse sources of data stands in the ability to distil and extract reliable and relevant information. Computer-based tools that use natural language processing and machine learning techniques have proven to help address such challenges. This current work proposes automatic reliable solutions for solving tasks that can help achieve a personalized-medicine, a medical practice that brings together general medical knowledge and case-specific medical information. Phenotypic medical observations, along with data coming from test results, are not enough when assessing and treating a medical case. Genetic, life-style, background and environmental data also need to be taken into
account in the medical decision process. This thesis’s goal is to prove that natural
language processing and machine learning techniques represent reliable solutions for
solving important medical-related problems.
From the numerous research problems that need to be answered when implementing
personalized medicine, the scope of this thesis is restricted to four, as follows:
1. Automatic identification of obesity-related diseases by using only textual clinical
data;
2. Automatic identification of relevant abstracts of published research to be used for
building systematic reviews;
3. Automatic identification of gene functions based on textual data of published medical abstracts;
4. Automatic identification and classification of important medical relations between medical concepts in clinical and technical data. This thesis investigation on finding automatic solutions for achieving a personalized medicine through information identification and extraction focused on individual specific problems that can be later linked in a puzzle-building manner. A diverse representation technique that follows a divide-and-conquer methodological approach shows to be the most reliable solution for building automatic models that solve the above mentioned
tasks. The methodologies that I propose are supported by in-depth research experiments
and thorough discussions and conclusions.
23
Doğru, Gökhan. "Terminological Quality Evaluation in Turkish to English Corpus-Based Machine Translation in Medical Domain." Doctoral thesis, Universitat Autònoma de Barcelona. Programa de Doctorat en Traducció i Estudis Interculturals, 2021. http://hdl.handle.net/10803/673337.
Full textAbstract:
Els aspectes generals de qualitat de la traducció automàtica (TA), com l’adequació i la fluïdesa, s’han estudiat àmpliament, però els aspectes més detallats, com la qualitat de la traducció de la terminologia, s’han subestimat, especialment en el context dels estudis de traducció. L’objectiu d’aquest estudi és analitzar els tipus i freqüències d’errors terminològics en la traducció automàtica estadística (TAE) i la traducció automàtica neuronal (TAN) personalitzades amb l’objectiu final de comprendre com el tipus de sistema de TA, el tipus de corpus i la mida del corpus afecten la qualitat de la traducció de terminologia.
Un corpus paral·lel turc-anglès obtingut a partir de resums de revistes de cardiologia va ser creat des de zero per entrenar motors de TAE i TAN de dominis específics. Després, aquest corpus es va combinar amb un corpus de domini mixt i es van entrenar dos motors més. Després de realitzar una avaluació automàtica i una avaluació humana en aquests 4 motors, els errors de terminologia es van anotar segons una tipologia d’errors de terminologia personalitzada. S’ha trobat que els tipus i freqüències dels errors terminològics són significativament diferents en els sistemes TAE i TAN, i que els canvis en la mida i tipus de corpus han tingut un impacte més dràstic en el TAN en comparació amb la TAE.
Una contribució clau de la dissertació a la investigació sobre TA és la tipologia d’error de terminologia independent del llenguatge per avaluar les fortaleses i debilitats relatives de diferents sistemes de TA en termes de terminologia. A més, la troballa que els sistemes TAN exhibeixen diferents tipus d’errors de terme amb diferents freqüències implica que les guies de postedició concebudes específicament per a sistemes TAE podrien requerir canvis per tal d’adaptar-se al nou patró de comportament de TAN.Los aspectos generales de calidad de la traducción automática (TA), como la adecuación y la fluidez, se han estudiado ampliamente, pero los aspectos más detallados, como la calidad de la traducción de la terminología, se han subestimado, especialmente en el contexto de los estudios de traducción. El objetivo de este estudio es analizar los tipos y frecuencias de errores terminológicos en la traducción automática estadística (TAE) y la traducción automática neuronal (TAN) con el objetivo final de comprender cómo el tipo de sistema de TA, el tipo de corpus y el tamaño del corpus afectan la calidad de la traducción de terminología. Un corpus paralelo turco-inglés obtenido a partir de resúmenes de revistas de cardiología se creó desde cero para entrenar motores de TAE y TAN de dominios específicos. Luego, este corpus se combina con un corpus de dominio mixto y se entrenaron dos motores más. Después de realizar una evaluación automática y una evaluación humana en estos 4 motores, los errores de terminología se anotaron según una tipología de errores de terminología personalizada. Se ha encontrado que los tipos y frecuencias de los errores terminológicos son significativamente diferentes en los sistemas TAE y TAN, y que los cambios en el tamaño y tipo de corpus tienen un impacto más drástico en el TAN en comparación con el TAE. Una contribución clave de la disertación es la tipología de error de terminología que se puede utilizar para evaluar las fortalezas y debilidades relativas de diferentes sistemas de TA en términos de terminología. Además, el hallazgo de que los sistemas TAN exhiben diferentes tipos de errores en los términos con diferentes frecuencias implica que las pautas de posedición que se prepararon para los textos resultantes de TAE deben actualizarse para adaptarse al nuevo patrón de comportamiento de TAN.
General quality aspects of machine translation (MT) such as adequacy and fluency are studied extensively, more fine-grained aspects such as the terminology translation quality have not received much attention especially in the context of translation studies. The objective of this study is to analyze the types and frequencies of terminology errors in custom statistical machine translation (SMT) and neural machine translation (NMT) with the goal of understanding how MT system type, corpus type and corpus size affect the terminology translation quality. A Turkish – English parallel corpus obtained from cardiology journal abstracts was built from scratch for training domain-specific SMT and NMT engines. Then, this domain-specific corpus is combined with a mixed domain corpus and two more engines were trained. After conducting automatic evaluation and human evaluation on these 4 engines, terminology errors were annotated based on a custom terminology error typology. It was found that the types and frequencies of terminology errors are significantly different in SMT and NMT systems, and that changes in corpus size and corpus type had more drastic impact on NMT compared to SMT. A key contribution of the dissertation to the MT research is the crafted language-agnostic terminology error typology which can be used for evaluating the relative strengths and weakness of different MT systems in terms of terminology. Besides, the finding that NMT systems exhibit different types of term errors with different frequencies implies that postediting guidelines conceived specifically for SMT systems could require changes to accommodate the behavior pattern of NMT.
Universitat Autònoma de Barcelona. Programa de Doctorat en Traducció i Estudis Interculturals
24
Ive, Julia. "Towards a Better Human-Machine Collaboration in Statistical Translation : Example of Systematic Medical Reviews." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS225/document.
Full textAbstract:
La traduction automatique (TA) a connu des progrès significatifs ces dernières années et continue de s'améliorer. La TA est utilisée aujourd'hui avec succès dans de nombreux contextes, y compris les environnements professionnels de traduction et les scénarios de production. Cependant, le processus de traduction requiert souvent des connaissances plus larges qu'extraites de corpus parallèles. Étant donné qu'une injection de connaissances humaines dans la TA est nécessaire, l'un des moyens possibles d'améliorer TA est d'assurer une collaboration optimisée entre l'humain et la machine. À cette fin, de nombreuses questions sont posées pour la recherche en TA: Comment détecter les passages où une aide humaine devrait être proposée ? Comment faire pour que les machines exploitent les connaissances humaines obtenues afin d'améliorer leurs sorties ? Enfin, comment optimiser l'échange: minimiser l'effort humain impliqué et maximiser la qualité de TA? Diverses solutions sont possibles selon les scénarios de traductions considérés. Dans cette thèse, nous avons choisi de nous concentrer sur la pré-édition, une intervention humaine en TA qui a lieu ex-ante, par opposition à la post-édition, où l'intervention humaine qui déroule ex-post. En particulier, nous étudions des scénarios de pré-édition ciblés où l'humain doit fournir des traductions pour des segments sources difficiles à traduire et choisis avec soin. Les scénarios de la pré-édition impliquant la pré-traduction restent étonnamment peu étudiés dans la communauté. Cependant, ces scénarios peuvent offrir une série d'avantages relativement, notamment, à des scénarios de post-édition non ciblés, tels que : la réduction de la charge cognitive requise pour analyser des phrases mal traduites; davantage de contrôle sur le processus; une possibilité que la machine exploite de nouvelles connaissances pour améliorer la traduction automatique au voisinage des segments pré-traduits, etc. De plus, dans un contexte multilingue, des difficultés communes peuvent être résolues simultanément pour de nombreuses langues. De tels scénarios s'adaptent donc parfaitement aux contextes de production standard, où l'un des principaux objectifs est de réduire le coût de l’intervention humaine et où les traductions sont généralement effectuées à partir d'une langue vers plusieurs langues à la fois. Dans ce contexte, nous nous concentrons sur la TA de revues systématiques en médecine. En considérant cet exemple, nous proposons une méthodologie indépendante du système pour la détection des difficultés de traduction. Nous définissons la notion de difficulté de traduction de la manière suivante : les segments difficiles à traduire sont des segments pour lesquels un système de TA fait des prédictions erronées. Nous formulons le problème comme un problème de classification binaire et montrons que, en utilisant cette méthodologie, les difficultés peuvent être détectées de manière fiable sans avoir accès à des informations spécifiques au système. Nous montrons que dans un contexte multilingue, les difficultés communes sont rares. Une perspective plus prometteuse en vue d'améliorer la qualité réside dans des approches dans lesquelles les traductions dans les différentes langues s’aident mutuellement à résoudre leurs difficultés. Nous intégrons les résultats de notre procédure de détection des difficultés dans un protocole de pré-édition qui permet de résoudre ces difficultés par pré-traduction. Nous évaluons le protocole dans un cadre simulé et montrons que la pré-traduction peut être à la fois utile pour améliorer la qualité de la TA et réaliste en termes d'implication des efforts humains. En outre, les effets indirects sont significatifs. Nous évaluons également notre protocole dans un contexte préliminaire impliquant des interventions humaines. Les résultats de ces expériences pilotes confirment les résultats obtenus dans le cadre simulé et ouvrent des perspectives encourageantes pour des tests ultérieuresMachine Translation (MT) has made significant progress in the recent years and continues to improve. Today, MT is successfully used in many contexts, including professional translation environments and production scenarios. However, the translation process requires knowledge larger in scope than what can be captured by machines even from a large quantity of translated texts. Since injecting human knowledge into MT is required, one of the potential ways to improve MT is to ensure an optimized human-machine collaboration. To this end, many questions are asked by modern research in MT: How to detect where human assistance should be proposed? How to make machines exploit the obtained human knowledge so that they could improve their output? And, not less importantly, how to optimize the exchange so as to minimize the human effort involved and maximize the quality of MT output? Various solutions have been proposed depending on concrete implementations of the MT process. In this thesis we have chosen to focus on Pre-Edition (PRE), corresponding to a type of human intervention into MT that takes place ex-ante, as opposed to Post-Edition (PE), where human intervention takes place ex-post. In particular, we study targeted PRE scenarios where the human is to provide translations for carefully chosen, difficult-to-translate, source segments. Targeted PRE scenarios involving pre-translation remain surprisingly understudied in the MT community. However, such PRE scenarios can offer a series of advantages as compared, for instance, to non-targeted PE scenarios: i.a., the reduction of the cognitive load required to analyze poorly translated sentences; more control over the translation process; a possibility that the machine will exploit new knowledge to improve the automatic translation of neighboring words, etc. Moreover, in a multilingual setting common difficulties can be resolved at one time and for many languages. Such scenarios thus perfectly fit standard production contexts, where one of the main goals is to reduce the cost of PE and where translations are commonly performed simultaneously from one language into many languages. A representative production context - an automatic translation of systematic medical reviews - is the focus of this work. Given this representative context, we propose a system-independent methodology for translation difficulty detection. We define the notion of translation difficulty as related to translation quality: difficult-to-translate segments are segments for which an MT system makes erroneous predictions. We cast the problem of difficulty detection as a binary classification problem and demonstrate that, using this methodology, difficulties can be reliably detected without access to system-specific information. We show that in a multilingual setting common difficulties are rare, and a better perspective of quality improvement lies in approaches where translations into different languages will help each other in the resolution of difficulties. We integrate the results of our difficulty detection procedure into a PRE protocol that enables resolution of those difficulties by pre-translation. We assess the protocol in a simulated setting and show that pre-translation as a type of PRE can be both useful to improve MT quality and realistic in terms of the human effort involved. Moreover, indirect effects are found to be genuine. We also assess the protocol in a preliminary real-life setting. Results of those pilot experiments confirm the results in the simulated setting and suggest an encouraging beginning of the test phase
25
Andersson, Olle. "Predicting Patient Length Of Stay at Time of Admission Using Machine Learning." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255150.
Full textAbstract:
This master thesis investigates the possibility of using machine learning methods to predict patient length of stay at the time of admission to a clinical ward from the emergency department. The main aim of this thesis is to provide a comparative analysis of different algorithms and to suggest a suitable model that can be used in a hospital prediction software. The results show that it is possible to achieve a balanced accuracy of 0.72 at the time of admission and of 0.75 at a later stage in the process. The suggested algorithm was Random Forest which combines good accuracy with effective training time, making it suitable for on-line use in a hospital. The study shows that there is a clear potential for the use of machine learning methods for predicting length of stay, but that further improvements have to be made before adaption into the healthcare.Detta masterexamensarbete utforskar möjligheten att använda maskin-inlärning för att förutspå vårdtiden för en patient då denne skrivs in på en vårdavdelning från akutvårds-avdelningen vid ett sjukhus. Huvudmålet för arbetet är att tillhandahålla en jämförelse av olika maskininlärnings-algoritmer och föreslå en algoritm som är lämplig att integrera i en mjukvara på sjukhuset. Resultaten visar att det är möjligt att nå en balanced accuracy på 0.72 vid inskrivningstillfället samt 0.75 vid en senare tidpunkt i vårdprocessen. Den föreslagna algoritmen var Random Forest som kombinerade bra prestanda med effektiv träningstid, något som gör den lämplig för att köras på sjukhuset. Projektet visar att det finns en tydlig potential för att använda maskininlärning för att prediktera vårdtid men att förbättringar krävs innan det kan nå hela vägen in i sjukhuset.
26
Al, Zamil Mohammed Gh I. "A Framework For Ranking And Categorizing Medical Documents." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611996/index.pdf.
Full textAbstract:
In this dissertation, we present a framework to enhance the retrieval, ranking, and categorization of text documents in medical domain. The contributions of this study are the introduction of a similarity model to retrieve and rank medical textdocuments and the introduction of rule-based categorization method based on lexical syntactic patterns features. We formulate the similarity model by combining three features to model the relationship among document and construct a document network. We aim to rank retrieved documents according to their topicsmaking highly relevant document on the top of the hit-list. We have applied this model on OHSUMED collection (TREC-9) in order to demonstrate the performance effectiveness in terms of topical ranking, recall, and precision metrics. In addition, we introduce ROLEX-SP (Rules Of LEXical Syntactic Patterns)
a method for the automatic induction of rule-based text-classifiers relies on lexical syntactic patterns as a set of features to categorize text-documents. The proposed method is dedicated to solve the problem of multi-class classification and feature imbalance problems in domain specific text documents. Furthermore, our proposed method is able to categorize documents according to a predefined set of characteristics such as: user-specific, domain-specific, and query-based categorization which facilitates browsing documents in search-engines and increase users ability to choose among relevant documents. To demonstrate the applicability of ROLEX-SP, we have performed experiments on OHSUMED (categorization collection). The results indicate that ROLEX-SP outperforms state-of-the-art methods in categorizing short-text medical documents.
27
Manivannan, Siyamalan. "Visual feature learning with application to medical image classification." Thesis, University of Dundee, 2015. https://discovery.dundee.ac.uk/en/studentTheses/10e26212-e836-4ccd-9b12-a576458de5eb.
Full textAbstract:
Various hand-crafted features have been explored for medical image classification, which include SIFT and Local Binary Patterns (LBP). However, hand-crafted features may not be optimally discriminative for classifying images from particular domains (e.g. colonoscopy), as not necessarily tuned to the domain’s characteristics. In this work, I give emphasis on learning highly discriminative local features and image representations to achieve the best possible classification performance for medical images, particularly for colonoscopy and histology (cell) images. I propose approaches to learn local features using unsupervised and weakly-supervised methods, and an approach to improve the feature encoding methods such as bag-of-words. Unlike the existing work, the proposed weakly-supervised approach uses image-level labels to learn the local features. Requiring image-labels instead of region-level labels makes annotations less expensive, and closer to the data normally available from normal clinical practice, hence more feasible in practice. In this thesis, first, I propose a generalised version of the LBP descriptor called the Generalised Local Ternary Patterns (gLTP), which is inspired by the success of LBP and its variants for colonoscopy image classification. gLTP is robust to both noise and illumination changes, and I demonstrate its competitive performance compared to the best performing LBP-based descriptors on two different datasets (colonoscopy and histology). However LBP-based descriptors (including gLTP) lose information due to the binarisation step involved in their construction. Therefore, I then propose a descriptor called the Extended Multi-Resolution Local Patterns (xMRLP), which is real-valued and reduces information loss. I propose unsupervised and weakly-supervised learning approaches to learn the set of parameters in xMRLP. I show that the learned descriptors give competitive or better performance compared to other descriptors such as root-SIFT and Random Projections. Finally, I propose an approach to improve feature encoding methods. The approach captures inter-cluster features, providing context information in the feature as well as in the image spaces, in addition to the intra-cluster features often captured by conventional feature encoding approaches. The proposed approaches have been evaluated on three datasets, 2-class colonoscopy (2, 100 images), 3-class colonoscopy (2, 800 images) and histology (public dataset, containing 13, 596 images). Some experiments on radiology images (IRMA dataset, public) also were given. I show state-of-the-art or superior classification performance on colonoscopy and histology datasets.
28
Deshpande, Hrishikesh. "Dictionary learning for pattern classification in medical imaging." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S032/document.
Full textAbstract:
La plupart des signaux naturels peuvent être représentés par une combinaison linéaire de quelques atomes dans un dictionnaire. Ces représentations parcimonieuses et les méthodes d'apprentissage de dictionnaires (AD) ont suscité un vif intérêt au cours des dernières années. Bien que les méthodes d'AD classiques soient efficaces dans des applications telles que le débruitage d'images, plusieurs méthodes d'AD discriminatifs ont été proposées pour obtenir des dictionnaires mieux adaptés à la classification. Dans ce travail, nous avons montré que la taille des dictionnaires de chaque classe est un facteur crucial dans les applications de reconnaissance des formes lorsqu'il existe des différences de variabilité entre les classes, à la fois dans le cas des dictionnaires classiques et des dictionnaires discriminatifs. Nous avons validé la proposition d'utiliser différentes tailles de dictionnaires, dans une application de vision par ordinateur, la détection des lèvres dans des images de visages, ainsi que par une application médicale plus complexe, la classification des lésions de scléroses en plaques (SEP) dans des images IRM multimodales. Les dictionnaires spécifiques à chaque classe sont appris pour les lésions et les tissus cérébraux sains. La taille du dictionnaire pour chaque classe est adaptée en fonction de la complexité des données. L'algorithme est validé à l'aide de 52 séquences IRM multimodales de 13 patients atteints de SEPMost natural signals can be approximated by a linear combination of a few atoms in a dictionary. Such sparse representations of signals and dictionary learning (DL) methods have received a special attention over the past few years. While standard DL approaches are effective in applications such as image denoising or compression, several discriminative DL methods have been proposed to achieve better image classification. In this thesis, we have shown that the dictionary size for each class is an important factor in the pattern recognition applications where there exist variability difference between classes, in the case of both the standard and discriminative DL methods. We validated the proposition of using different dictionary size based on complexity of the class data in a computer vision application such as lips detection in face images, followed by more complex medical imaging application such as classification of multiple sclerosis (MS) lesions using MR images. The class specific dictionaries are learned for the lesions and individual healthy brain tissues, and the size of the dictionary for each class is adapted according to the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients
29
Bardolet, Pettersson Susana. "Managing imbalanced training data by sequential segmentation in machine learning." Thesis, Linköpings universitet, Avdelningen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-155091.
Full textAbstract:
Imbalanced training data is a common problem in machine learning applications. Thisproblem refers to datasets in which the foreground pixels are significantly fewer thanthe background pixels. By training a machine learning model with imbalanced data, theresult is typically a model that classifies all pixels as the background class. A result thatindicates no presence of a specific condition when it is actually present is particularlyundesired in medical imaging applications. This project proposes a sequential system oftwo fully convolutional neural networks to tackle the problem. Semantic segmentation oflung nodules in thoracic computed tomography images has been performed to evaluate theperformance of the system. The imbalanced data problem is present in the training datasetused in this project, where the average percentage of pixels belonging to the foregroundclass is 0.0038 %. The sequential system achieved a sensitivity of 83.1 % representing anincrease of 34 % compared to the single system. The system only missed 16.83% of thenodules but had a Dice score of 21.6 % due to the detection of multiple false positives. Thismethod shows considerable potential to be a solution to the imbalanced data problem withcontinued development.
30
Wallis, David. "A study of machine learning and deep learning methods and their application to medical imaging." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPAST057.
Full textAbstract:
Nous utilisons d'abord des réseaux neuronaux convolutifs (CNNs) pour automatiser la détection des ganglions lymphatiques médiastinaux dans les images TEP/TDM. Nous construisons un modèle entièrement automatisé pour passer directement des images TEP/TDM à la localisation des ganglions. Les résultats montrent une performance comparable à celle d'un médecin. Dans la seconde partie de la thèse, nous testons la performance, l'interprétabilité et la stabilité des modèles radiomiques et CNN sur trois ensembles de données (IRM cérébrale 2D, TDM pulmonaire 3D, TEP/TDM médiastinale 3D). Nous comparons la façon dont les modèles s'améliorent lorsque davantage de données sont disponibles et nous examinons s'il existe des tendances communess aux différents problèmes. Nous nous demandons si les méthodes actuelles d'interprétation des modèles sont satisfaisantes. Nous étudions également comment une segmentation précise affecte les performances des modèles. Nous utilisons d'abord des réseaux neuronaux convolutifs (CNNs) pour automatiser la détection des ganglions lymphatiques médiastinaux dans les images TEP/TDM. Nous construisons un modèle entièrement automatisé pour passer directement des images TEP/TDM à la localisation des ganglions. Les résultats montrent une performance comparable à celle d'un médecin. Dans la seconde partie de la thèse, nous testons la performance, l'interprétabilité et la stabilité des modèles radiomiques et CNN sur trois ensembles de données (IRM cérébrale 2D, TDM pulmonaire 3D, TEP/TDM médiastinale 3D). Nous comparons la façon dont les modèles s'améliorent lorsque davantage de données sont disponibles et nous examinons s'il existe des tendances communess aux différents problèmes. Nous nous demandons si les méthodes actuelles d'interprétation des modèles sont satisfaisantes. Nous étudions également comment une segmentation précise affecte les performances des modèlesWe first use Convolutional Neural Networks (CNNs) to automate mediastinal lymph node detection using FDG-PET/CT scans. We build a fully automated model to go directly from whole-body FDG-PET/CT scans to node localisation. The results show a comparable performance to an experienced physician. In the second half of the thesis we experimentally test the performance, interpretability, and stability of radiomic and CNN models on three datasets (2D brain MRI scans, 3D CT lung scans, 3D FDG-PET/CT mediastinal scans). We compare how the models improve as more data is available and examine whether there are patterns common to the different problems. We question whether current methods for model interpretation are satisfactory. We also investigate how precise segmentation affects the performance of the models. We first use Convolutional Neural Networks (CNNs) to automate mediastinal lymph node detection using FDG-PET/CT scans. We build a fully automated model to go directly from whole-body FDG-PET/CT scans to node localisation. The results show a comparable performance to an experienced physician. In the second half of the thesis we experimentally test the performance, interpretability, and stability of radiomic and CNN models on three datasets (2D brain MRI scans, 3D CT lung scans, 3D FDG-PET/CT mediastinal scans). We compare how the models improve as more data is available and examine whether there are patterns common to the different problems. We question whether current methods for model interpretation are satisfactory. We also investigate how precise segmentation affects the performance of the models
31
Bustos, Aurelia. "Extraction of medical knowledge from clinical reports and chest x-rays using machine learning techniques." Doctoral thesis, Universidad de Alicante, 2019. http://hdl.handle.net/10045/102193.
Full textAbstract:
This thesis addresses the extraction of medical knowledge from clinical text using deep learning techniques. In particular, the proposed methods focus on cancer clinical trial protocols and chest x-rays reports. The main results are a proof of concept of the capability of machine learning methods to discern which are regarded as inclusion or exclusion criteria in short free-text clinical notes, and a large scale chest x-ray image dataset labeled with radiological findings, diagnoses and anatomic locations. Clinical trials provide the evidence needed to determine the safety and effectiveness of new medical treatments. These trials are the basis employed for clinical practice guidelines and greatly assist clinicians in their daily practice when making decisions regarding treatment. However, the eligibility criteria used in oncology trials are too restrictive. Patients are often excluded on the basis of comorbidity, past or concomitant treatments and the fact they are over a certain age, and those patients that are selected do not, therefore, mimic clinical practice. This signifies that the results obtained in clinical trials cannot be extrapolated to patients if their clinical profiles were excluded from the clinical trial protocols. The efficacy and safety of new treatments for patients with these characteristics are not, therefore, defined. Given the clinical characteristics of particular patients, their type of cancer and the intended treatment, discovering whether or not they are represented in the corpus of available clinical trials requires the manual review of numerous eligibility criteria, which is impracticable for clinicians on a daily basis. In this thesis, a large medical corpora comprising all cancer clinical trials protocols in the last 18 years published by competent authorities was used to extract medical knowledge in order to help automatically learn patient’s eligibility in these trials. For this, a model is built to automatically predict whether short clinical statements were considered inclusion or exclusion criteria. A method based on deep neural networks is trained on a dataset of 6 million short free-texts to classify them between elegible or not elegible. For this, pretrained word embeddings were used as inputs in order to predict whether or not short free-text statements describing clinical information were considered eligible. The semantic reasoning of the word-embedding representations obtained was also analyzed, being able to identify equivalent treatments for a type of tumor in an analogy with the drugs used to treat other tumors. Results show that representation learning using deep neural networks can be successfully leveraged to extract the medical knowledge from clinical trial protocols and potentially assist practitioners when prescribing treatments. The second main task addressed in this thesis is related to knowledge extraction from medical reports associated with radiographs. Conventional radiology remains the most performed technique in radiodiagnosis services, with a percentage close to 75% (Radiología Médica, 2010). In particular, chest x-ray is the most common medical imaging exam with over 35 million taken every year in the US alone (Kamel et al., 2017). They allow for inexpensive screening of several pathologies including masses, pulmonary nodules, effusions, cardiac abnormalities and pneumothorax. For this task, all the chest-x rays that had been interpreted and reported by radiologists at the Hospital Universitario de San Juan (Alicante) from Jan 2009 to Dec 2017 were used to build a novel large-scale dataset in which each high-resolution radiograph is labeled with its corresponding metadata, radiological findings and pathologies. This dataset, named PadChest, includes more than 160,000 images obtained from 67,000 patients, covering six different position views and additional information on image acquisition and patient demography. The free text reports written in Spanish by radiologists were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. For this, a subset of the reports (a 27%) were manually annotated by trained physicians, whereas the remaining set was automatically labeled with deep supervised learning methods using attention mechanisms and fed with the text reports. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray databases suitable for training supervised models concerning radiographs, and also the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded on request from http://bimcv.cipf.es/bimcv-projects/padchest/. PadChest is intended for training image classifiers based on deep learning techniques to extract medical knowledge from chest x-rays. It is essential that automatic radiology reporting methods could be integrated in a clinically validated manner in radiologists’ workflow in order to help specialists to improve their efficiency and enable safer and actionable reporting. Computer vision methods capable of identifying both the large spectrum of thoracic abnormalities (and also the normality) need to be trained on large-scale comprehensively labeled large-scale x-ray datasets such as PadChest. The development of these computer vision tools, once clinically validated, could serve to fulfill a broad range of unmet needs. Beyond implementing and obtaining results for both clinical trials and chest x-rays, this thesis studies the nature of the health data, the novelty of applying deep learning methods to obtain large-scale labeled medical datasets, and the relevance of its applications in medical research, which have contributed to its extramural diffusion and worldwide reach. This thesis describes this journey so that the reader is navigated across multiple disciplines, from engineering to medicine up to ethical considerations in artificial intelligence applied to medicine.
32
Alzubaidi, Laith. "Deep learning for medical imaging applications." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227812/1/Laith_Alzubaidi_Thesis.pdf.
Full textAbstract:
This thesis investigated novel deep learning techniques for advanced medical imaging applications. It addressed three major research issues of employing deep learning for medical imaging applications including network architecture, lack of training data, and generalisation. It proposed three new frameworks for CNN network architecture and three novel transfer learning methods. The proposed solutions have been tested on four different medical imaging applications demonstrating their effectiveness and generalisation. These solutions have already been employed by the scientific community showing excellent performance in medical imaging applications and other domains.
33
Havaei, Seyed Mohammad. "Machine learning methods for brain tumor segmentation." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10260.
Full textAbstract:
Abstract : Malignant brain tumors are the second leading cause of cancer related deaths in children under 20. There are nearly 700,000 people in the U.S. living with a brain tumor and 17,000 people are likely to loose their lives due to primary malignant and central nervous system brain tumor every year.
To identify whether a patient is diagnosed with brain tumor in a non-invasive way, an MRI scan of the brain is acquired followed by a manual examination of the scan by an expert who looks for lesions (i.e. cluster of cells which deviate from healthy tissue). For treatment purposes, the tumor and its sub-regions are outlined in a procedure known as brain tumor segmentation . Although brain tumor segmentation is primarily done manually, it is very time consuming and the segmentation is subject to variations both between observers and within the same observer. To address these issues, a number of automatic and semi-automatic methods have been proposed over the years to help physicians in the decision making process.
Methods based on machine learning have been subjects of great interest in brain tumor segmentation. With the advent of deep learning methods and their success in many computer vision applications such as image classification, these methods have also started to gain popularity in medical image analysis.
In this thesis, we explore different machine learning and deep learning methods applied to brain tumor segmentation.Résumé: Les tumeurs malignes au cerveau sont la deuxième cause principale de décès chez les enfants de moins de 20 ans. Il y a près de 700 000 personnes aux États-Unis vivant avec une tumeur au cerveau, et 17 000 personnes sont chaque année à risque de perdre leur vie suite à une tumeur maligne primaire dans le système nerveu central. Pour identifier de façon non-invasive si un patient est atteint d'une tumeur au cerveau, une image IRM du cerveau est acquise et analysée à la main par un expert pour trouver des lésions (c.-à-d. un groupement de cellules qui diffère du tissu sain). Une tumeur et ses régions doivent être détectées à l'aide d'une segmentation pour aider son traitement. La segmentation de tumeur cérébrale et principalement faite à la main, c'est une procédure qui demande beaucoup de temps et les variations intra et inter expert pour un même cas varient beaucoup. Pour répondre à ces problèmes, il existe beaucoup de méthodes automatique et semi-automatique qui ont été proposés ces dernières années pour aider les praticiens à prendre des décisions. Les méthodes basées sur l'apprentissage automatique ont suscité un fort intérêt dans le domaine de la segmentation des tumeurs cérébrales. L'avènement des méthodes de Deep Learning et leurs succès dans maintes applications tels que la classification d'images a contribué à mettre de l'avant le Deep Learning dans l'analyse d'images médicales. Dans cette thèse, nous explorons diverses méthodes d'apprentissage automatique et de Deep Learning appliquées à la segmentation des tumeurs cérébrales.
34
Dokania, Puneet Kumar. "High-Order Inference, Ranking, and Regularization Path for Structured SVM." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC044/document.
Full textAbstract:
Cette thèse présente de nouvelles méthodes pour l'application de la prédiction structurée en vision numérique et en imagerie médicale.Nos nouvelles contributions suivent quatre axes majeurs.La première partie de cette thèse étudie le problème d'inférence d'ordre supérieur.Nous présentons une nouvelle famille de problèmes de minimisation d'énergie discrète, l'étiquetage parcimonieux, encourageant la parcimonie des étiquettes.C'est une extension naturelle des problèmes connus d'étiquetage de métriques aux potentiels d'ordre élevé.Nous proposons par ailleurs une généralisation du modèle Pn-Potts, le modèle Pn-Potts hiérarchique.Enfin, nous proposons un algorithme parallélisable à proposition de mouvements avec de fortes bornes multiplicatives pour l'optimisation du modèle Pn-Potts hiérarchique et l'étiquetage parcimonieux.La seconde partie de cette thèse explore le problème de classement en utilisant de l'information d'ordre élevé.Nous introduisons deux cadres différents pour l'incorporation d'information d'ordre élevé dans le problème de classement.Le premier modèle, que nous nommons SVM binaire d'ordre supérieur (HOB-SVM), optimise une borne supérieure convexe sur l'erreur 0-1 pondérée tout en incorporant de l'information d'ordre supérieur en utilisant un vecteur de charactéristiques jointes.Le classement renvoyé par HOB-SVM est obtenu en ordonnant les exemples selon la différence entre la max-marginales de l'affectation d'un exemple à la classe associée et la max-marginale de son affectation à la classe complémentaire.Le second modèle, appelé AP-SVM d'ordre supérieur (HOAP-SVM), s'inspire d'AP-SVM et de notre premier modèle, HOB-SVM.Le modèle correspond à une optimisation d'une borne supérieure sur la précision moyenne, à l'instar d'AP-SVM, qu'il généralise en permettant également l'incorporation d'information d'ordre supérieur.Nous montrons comment un optimum local du problème d'apprentissage de HOAP-SVM peut être déterminé efficacement grâce à la procédure concave-convexe.En utilisant des jeux de données standards, nous montrons empiriquement que HOAP-SVM surpasse les modèles de référence en utilisant efficacement l'information d'ordre supérieur tout en optimisant directement la fonction d'erreur appropriée.Dans la troisième partie, nous proposons un nouvel algorithme, SSVM-RP, pour obtenir un chemin de régularisation epsilon-optimal pour les SVM structurés.Nous présentons également des variantes intuitives de l'algorithme Frank-Wolfe pour l'optimisation accélérée de SSVM-RP.De surcroît, nous proposons une approche systématique d'optimisation des SSVM avec des contraintes additionnelles de boîte en utilisant BCFW et ses variantes.Enfin, nous proposons un algorithme de chemin de régularisation pour SSVM avec des contraintes additionnelles de positivité/negativité.Dans la quatrième et dernière partie de la thèse, en appendice, nous montrons comment le cadre de l'apprentissage semi-supervisé des SVM à variables latentes peut être employé pour apprendre les paramètres d'un problème complexe de recalage déformable.Nous proposons un nouvel algorithme discriminatif semi-supervisé pour apprendre des métriques de recalage spécifiques au contexte comme une combinaison linéaire des métriques conventionnelles.Selon l'application, les métriques traditionnelles sont seulement partiellement sensibles aux propriétés anatomiques des tissus.Dans ce travail, nous cherchons à déterminer des métriques spécifiques à l'anatomie et aux tissus, par agrégation linéaire de métriques connues.Nous proposons un algorithme d'apprentissage semi-supervisé pour estimer ces paramètres conditionnellement aux classes sémantiques des données, en utilisant un jeu de données faiblement annoté.Nous démontrons l'efficacité de notre approche sur trois jeux de données particulièrement difficiles dans le domaine de l'imagerie médicale, variables en terme de structures anatomiques et de modalités d'imagerieThis thesis develops novel methods to enable the use of structured prediction in computer vision and medical imaging. Specifically, our contributions are four fold. First, we propose a new family of high-order potentials that encourage parsimony in the labeling, and enable its use by designing an accurate graph cuts based algorithm to minimize the corresponding energy function. Second, we show how the average precision SVM formulation can be extended to incorporate high-order information for ranking. Third, we propose a novel regularization path algorithm for structured SVM. Fourth, we show how the weakly supervised framework of latent SVM can be employed to learn the parameters for the challenging deformable registration problem.In more detail, the first part of the thesis investigates the high-order inference problem. Specifically, we present a novel family of discrete energy minimization problems, which we call parsimonious labeling. It is a natural generalization of the well known metric labeling problems for high-order potentials. In addition to this, we propose a generalization of the Pn-Potts model, which we call Hierarchical Pn-Potts model. In the end, we propose parallelizable move making algorithms with very strong multiplicative bounds for the optimization of the hierarchical Pn-Potts model and the parsimonious labeling.Second part of the thesis investigates the ranking problem while using high-order information. Specifically, we introduce two alternate frameworks to incorporate high-order information for the ranking tasks. The first framework, which we call high-order binary SVM (HOB-SVM), optimizes a convex upperbound on weighted 0-1 loss while incorporating high-order information using joint feature map. The rank list for the HOB-SVM is obtained by sorting samples using max-marginals based scores. The second framework, which we call high-order AP-SVM (HOAP-SVM), takes its inspiration from AP-SVM and HOB-SVM (our first framework). Similar to AP-SVM, it optimizes upper bound on average precision. However, unlike AP-SVM and similar to HOB-SVM, it can also encode high-order information. The main disadvantage of HOAP-SVM is that estimating its parameters requires solving a difference-of-convex program. We show how a local optimum of the HOAP-SVM learning problem can be computed efficiently by the concave-convex procedure. Using standard datasets, we empirically demonstrate that HOAP-SVM outperforms the baselines by effectively utilizing high-order information while optimizing the correct loss function.In the third part of the thesis, we propose a new algorithm SSVM-RP to obtain epsilon-optimal regularization path of structured SVM. We also propose intuitive variants of the Block-Coordinate Frank-Wolfe algorithm (BCFW) for the faster optimization of the SSVM-RP algorithm. In addition to this, we propose a principled approach to optimize the SSVM with additional box constraints using BCFW and its variants. In the end, we propose regularization path algorithm for SSVM with additional positivity/negativity constraints.In the fourth and the last part of the thesis (Appendix), we propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional metrics. Conventional metrics can cope partially - depending on the clinical context - with tissue anatomical properties. In this work we seek to determine anatomy/tissue specific metrics as a context-specific aggregation/linear combination of known metrics. We propose a weakly supervised learning algorithm for estimating these parameters conditionally to the data semantic classes, using a weak training dataset. We show the efficacy of our approach on three highly challenging datasets in the field of medical imaging, which vary in terms of anatomical structures and image modalities
35
Wallner, Vanja. "Mapping medical expressions to MedDRA using Natural Language Processing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-426916.
Full textAbstract:
Pharmacovigilance, also referred to as drug safety, is an important science for identifying risks related to medicine intake. Side effects of medicine can be caused by for example interactions, high dosage and misuse. In order to find patterns in what causes the unwanted effects, information needs to be gathered and mapped to predefined terms. This mapping is today done manually by experts which can be a very difficult and time consuming task. In this thesis the aim is to automate the process of mapping side effects by using machine learning techniques. The model was developed using information from preexisting mappings of verbatim expressions of side effects. The final model that was constructed made use of the pre-trained language model BERT, which has received state-of-the-art results within the NLP field. When evaluating on the test set the final model performed an accuracy of 80.21%. It was found that some verbatims were very difficult for our model to classify mainly because of ambiguity or lack of information contained in the verbatim. As it is very important for the mappings to be done correctly, a threshold was introduced which left for manual mapping the verbatims that were most difficult to classify. This process could however still be improved as suggested terms were generated from the model, which could be used as support for the specialist responsible for the manual mapping.
36
Kanwal, Summrina. "Towards a novel medical diagnosis system for clinical decision support system applications." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/25397.
Full textAbstract:
Clinical diagnosis of chronic disease is a vital and challenging research problem which requires intensive clinical practice guidelines in order to ensure consistent and efficient patient care. Conventional medical diagnosis systems inculcate certain limitations, like complex diagnosis processes, lack of expertise, lack of well described procedures for conducting diagnoses, low computing skills, and so on. Automated clinical decision support system (CDSS) can help physicians and radiologists to overcome these challenges by combining the competency of radiologists and physicians with the capabilities of computers. CDSS depend on many techniques from the fields of image acquisition, image processing, pattern recognition, machine learning as well as optimization for medical data analysis to produce efficient diagnoses. In this dissertation, we discuss the current challenges in designing an efficient CDSS as well as a number of the latest techniques (while identifying best practices for each stage of the framework) to meet these challenges by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest and thus aiding in medical diagnosis. To meet these challenges, we propose an extension of conventional clinical decision support system framework, by incorporating artificial immune network (AIN) based hyper-parameter optimization as integral part of it. We applied the conventional as well as optimized CDSS on four case studies (most of them comprise medical images) for efficient medical diagnosis and compared the results. The first key contribution is the novel application of a local energy-based shape histogram (LESH) as the feature set for the recognition of abnormalities in mammograms. We investigated the implication of this technique for the mammogram datasets of the Mammographic Image Analysis Society and INbreast. In the evaluation, regions of interest were extracted from the mammograms, their LESH features were calculated, and they were fed to support vector machine (SVM) and echo state network (ESN) classifiers. In addition, the impact of selecting a subset of LESH features based on the classification performance was also observed and benchmarked against a state-of-the-art wavelet based feature extraction method. The second key contribution is to apply the LESH technique to detect lung cancer. The JSRT Digital Image Database of chest radiographs was selected for research experimentation. Prior to LESH feature extraction, we enhanced the radiograph images using a contrast limited adaptive histogram equalization (CLAHE) approach. Selected state-of-the-art cognitive machine learning classifiers, namely the extreme learning machine (ELM), SVM and ESN, were then applied using the LESH extracted features to enable the efficient diagnosis of a correct medical state (the existence of benign or malignant cancer) in the x-ray images. Comparative simulation results, evaluated using the classification accuracy performance measure, were further benchmarked against state-of-the-art wavelet based features, and authenticated the distinct capability of our proposed framework for enhancing the diagnosis outcome. As the third contribution, this thesis presents a novel technique for detecting breast cancer in volumetric medical images based on a three-dimensional (3D) LESH model. It is a hybrid approach, and combines the 3D LESH feature extraction technique with machine learning classifiers to detect breast cancer from MRI images. The proposed system applies CLAHE to the MRI images before extracting the 3D LESH features. Furthermore, a selected subset of features is fed to a machine learning classifier, namely the SVM, ELM or ESN, to detect abnormalities and to distinguish between different stages of abnormality. The results indicate the high performance of the proposed system. When compared with the wavelet-based feature extraction technique, statistical analysis testifies to the significance of our proposed algorithm. The fourth contribution is a novel application of the (AIN) for optimizing machine learning classification algorithms as part of CDSS. We employed our proposed technique in conjunction with selected machine learning classifiers, namely the ELM, SVM and ESN, and validated it using the benchmark medical datasets of PIMA India diabetes and BUPA liver disorders, two-dimensional (2D) medical images, namely MIAS and INbreast and JSRT chest radiographs, as well as on the three-dimensional TCGA-BRCA breast MRI dataset. The results were investigated using the classification accuracy measure and the learning time. We also compared our methodology with the benchmarked multi-objective genetic algorithm (ES)-based optimization technique. The results authenticate the potential of the AIN optimised CDSS.
37
Kavanagh, Alan. "Study of improved casting methods for the manufacture of medical grade cobalt alloy." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7488/.
Full textAbstract:
Within many foundries, poor mould design and casting methods result in high levels of process variability, poor metal yield, scrap and inefficiencies leading to overall sub-optimal performance. The aim of this project was to try to try to address main problem areas through an alternate casting method (1-tree casting) used for the manufacture of ASTM F-75 cobalt chrome (Co-Cr) biomedical castings. The mould filling of various runner systems was assessed using real-time X-ray imaging and computational modelling. Mechanical testing, CT-scanning and metallurgical inspection of as-cast and heat-treated test bars produced in industrial trials at the DePuy foundry were performed. Direct thermocouple measurements, thermal imaging and microstructure measurements examined the effect of casting method on solidification time and cooling rates. Numerical modelling using ProCAST casting simulation software was performed. A statistical improvement in the as-cast tensile strength was observed with the 1-tree casting method compared to the established casting method. CT analysis indicated the presence of discrete gas porosity in some specimens which was attributed to high levels of air entrainment during pouring. The occurrence rate and morphology of the observed pores is described. Post heat-treatment the differences in the as-cast mechanical properties were eliminated with no evidence of casting method observed. However elongation to fracture results in both the as-cast and heat-treated conditions were lower than expected, and pose a challenge regardless of casting method. The 1-tree casting method reduced variation in alloy cooling rates and solidification times versus the established process.
38
Drusiani, Alberto. "Deep Learning Text Classification for Medical Diagnosis." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17281/.
Full textAbstract:
The ICD coding is the international standard for the classification of diseases and related disorders, drawn up by the World Health Organization. It was introduced to simplify the exchange of medical data, to speed up statistical analyzes and to make insurance reimbursements efficient.
The manual classification of ICD-9-CM codes still requires a human effort that implies a considerable waste of resources and for this reason several methods have been presented over the years to automate the process.
In this thesis an approach is proposed for the automatic classification of medical diagnoses in ICD-9-CM codes using the Recurrent Neural Networks, in particular the LSTM module, and exploiting the word embedding. The results were satisfactory as we were able to obtain better accuracy than Support Vector Machines, the most used traditional method. Furthermore, we have shown the effectiveness of specific domain embedding models compared to general ones.
39
Rahman, M. Mostafizur. "Machine learning based data pre-processing for the purpose of medical data mining and decision support." Thesis, University of Hull, 2014. http://hydra.hull.ac.uk/resources/hull:10103.
Full textAbstract:
Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. Sometimes, improved data quality is itself the goal of the analysis, usually to improve processes in a production database and the designing of decision support. As medicine moves forward there is a need for sophisticated decision support systems that make use of data mining to support more orthodox knowledge engineering and Health Informatics practice. However, the real-life medical data rarely complies with the requirements of various data mining tools. It is often inconsistent, noisy, containing redundant attributes, in an unsuitable format, containing missing values and imbalanced with regards to the outcome class label. Many real-life data sets are incomplete, with missing values. In medical data mining the problem with missing values has become a challenging issue. In many clinical trials, the medical report pro-forma allow some attributes to be left blank, because they are inappropriate for some class of illness or the person providing the information feels that it is not appropriate to record the values for some attributes. The research reported in this thesis has explored the use of machine learning techniques as missing value imputation methods. The thesis also proposed a new way of imputing missing value by supervised learning. A classifier was used to learn the data patterns from a complete data sub-set and the model was later used to predict the missing values for the full dataset. The proposed machine learning based missing value imputation was applied on the thesis data and the results are compared with traditional Mean/Mode imputation. Experimental results show that all the machine learning methods which we explored outperformed the statistical method (Mean/Mode). The class imbalance problem has been found to hinder the performance of learning systems. In fact, most of the medical datasets are found to be highly imbalance in their class label. The solution to this problem is to reduce the gap between the minority class samples and the majority class samples. Over-sampling can be applied to increase the number of minority class sample to balance the data. The alternative to over-sampling is under-sampling where the size of majority class sample is reduced. The thesis proposed one cluster based under-sampling technique to reduce the gap between the majority and minority samples. Different under-sampling and over-sampling techniques were explored as ways to balance the data. The experimental results show that for the thesis data the new proposed modified cluster based under-sampling technique performed better than other class balancing techniques. In further research it is found that the class imbalance problem not only affects the classification performance but also has an adverse effect on feature selection. The thesis proposed a new framework for feature selection for class imbalanced datasets. The research found that, using the proposed framework the classifier needs less attributes to show high accuracy, and more attributes are needed if the data is highly imbalanced. The research described in the thesis contains the flowing four novel main contributions. a) Improved data mining methodology for mining medical data b) Machine learning based missing value imputation method c) Cluster Based semi-supervised class balancing method d) Feature selection framework for class imbalance datasets The performance analysis and comparative study show that the use of proposed method of missing value imputation, class balancing and feature selection framework can provide an effective approach to data preparation for building medical decision support.
40
Feng, Yunyi. "Identification of Medical Coding Errors and Evaluation of Representation Methods for Clinical Notes Using Machine Learning." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1555421482252775.
Full text
41
Hartland, Joanne. "The machinery of medicine : an analysis of algorithmic approaches to medical knowledge and practice." Thesis, University of Bath, 1993. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357868.
Full text
42
Nyongesa, Henry Okola. "Genetic based machine learning allied to multi-variable fuzzy control of anaesthesia." Thesis, University of Sheffield, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295759.
Full text
43
Verzellesi, Laura. "Metodiche di statistical e machine learning per ananlisi di immagini mediche." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19145/.
Full textAbstract:
Questa tesi ha come obiettivo la creazione e l’implementazione di un codice Python per la classificazione dei tessuti all’interno di un’immagine medica in formato DCOM. A tale scopo, sono adottate le immagini mediche PET-CT fusion per il loro vantaggio nel fornire informazioni sia anatomiche che funzionali. Sono complessivamente importate e analizzate 6 immagini (set di slices): 5 di queste sono utilizzate per il labelling e la rimanente parte è usata per la predizione del modello. Dopo una breve descrizione delle funzioni matematiche alla base del calcolo ed estrazione delle features di Haralick (la matrice di co-occorrenza, misura posizionale dei livelli di grigio di un’immagine, e le 14 features di Haralick), viene presentata in dettaglio l’implementazione di tali funzioni nel codice e le modalità con cui sono utilizzate quest’ultime con la finalità di riconoscere i diversi tipi di tessuti. Sono illustrati gli step progettuali della creazione dei dataset delle label e delle features e della selezione e allenamento del miglior modello per la classificazione. L’approccio seguito permette di classificare diversi pixel di un’immagine DCOM nelle classi “bone”, ossa, “organ”, organi, e “background”, sfondo. Possibili future implementazioni della metodologia adottata comprendono la creazione di nuove classi per affinare la classificazione dei diversi tessuti.
44
Kleine, Klaus. "Micromachining with single mode fibre lasers for medical device production." Thesis, University of Liverpool, 2009. http://livrepository.liverpool.ac.uk/1295/.
Full textAbstract:
This Thesis is based on several research and development programs to implement the use of fibre lasers in the manufacturing of medical devices like stents and pacemakers. In general, the medical device manufacturing industry has a high demand for laser micromachining applications. The content of the thesis describes laser micromachining of metallic components with single mode fibre lasers. At the started of the research work for this thesis, most laser machining processes used flash-lamp pumped solid-state lasers for those applications. Reliable laser operation and low maintenance are required to meet the yields and up-time requirements for medical devices, such as stent cutting and pacemaker welding. Many lasers for micromachining applications are configured to operate near the diffraction limited beam performance to achieve very small feature sizes. It is challenging to maintain such a laser system performance in a production environment. The fibre laser provides a number of attractive features that could address the needs to maintain high up-time and high yields: • A single mode fibre laser does not require mirror alignment. • Diode pumped fibre lasers reduce maintenance due to eliminating the lamp change. • The compact air-cooled design helps to save expensive clean room space on the production floor. By 2000 the increases in average laser power extended the use of the fibre lasers into industrial applications such as cutting and welding.. The lasers investigated in this thesis generated 50 W to 200 W of laser power, representing the highest power levels commercially available at that time. For the microcutting of medical implants such as stents and guide wires, kerf width and sidewall surface quality are of special interest. Developing processes capable of achieving these criteria was the primary objective of the research described in this thesis. A secondary concern is the heat affected zone created by the laser machining process. Operation conditions to minimize this effect are also discussed in this thesis. Many microwelding applications in the electronics, telecom and medical device industry require smaller and smaller laser joining areas. The quality of a laser welded joint is very dependant on the temporal and spatial parameters of the laser beam. These parameters must be adjusted to match to the processing speed and the materials being welded. Switching continuous wave fibre lasers can achieve the parameters for processes requiring low average power. However the pulse-to-pulse stability can effect the process and has been investigated. Some welding applications require focus spot diameters in the order of 50 μm and pulse energy levels as low as 10 mJ. The fibre laser’s excellent single mode beam quality provides the desired spot size and laser power density. The research summarized in this thesis was performed to prove that fibre lasers are viable tools for micromachining. This thesis compares fibre laser machining results with those using legacy laser processes and describes ways to improve the quality of the fibre laser machining process.
45
Lin, Laura. "Applying human factors engineering to medical device design, an empirical evaluation of patient-controlled analgesia machine interfaces." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ29431.pdf.
Full text
46
McGowan, Martin. "The development of an inline machine vision inspection system for operation in a medical device manufacturing facility." Thesis, Glasgow Caledonian University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443252.
Full text
47
Kabir, Md Faisal. "Extracting Useful Information and Building Predictive Models from Medical and Health-Care Data Using Machine Learning Techniques." Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/31924.
Full textAbstract:
In healthcare, a large number of medical data has emerged. To effectively use these data to improve healthcare outcomes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various machine learning (ML) and data mining (DM) methods that could be applied to the type of data sets that are available in the healthcare area.
The first part of the dissertation investigates DM methods on healthcare or medical data to find significant information in the form of rules. Class association rule mining, a variant of association rule mining, was used to obtain the rules with some targeted items or class labels. These rules can be used to improve public awareness of different cancer symptoms and could also be useful to initiate prevention strategies.
In the second part of the thesis, ML techniques have been applied in healthcare or medical data to build a predictive model. Three different classification techniques on a real-world breast cancer risk factor data set have been investigated. Due to the imbalance characteristics of the data set various resampling methods were used before applying the classifiers. It is shown that there was a significant improvement in performance when applying a resampling technique as compared to applying no resampling technique.
Moreover, super learning technique that uses multiple base learners, have been investigated to boost the performance of classification models. Two different forms of super learner have been investigated - the first one uses two base learners while the second one uses three base learners. The models were then evaluated against well-known benchmark data sets related to the healthcare domain and the results showed that the SL model performs better than the individual classifier and the baseline ensemble.
Finally, we assessed cancer-relevant genes of prostate cancer with the most significant correlations with the clinical outcome of the sample type and the overall survival. Rules from the RNA-sequencing of prostate cancer patients was discovered. Moreover, we built the regression model and from the model rules for predicting the survival time of patients were generated.
48
Ansved, Linn, and Karin Eklann. "Exploring ways to convey medical information during digital triage : A combined user research and machine learning approach." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-386420.
Full textAbstract:
The aim of this project was to investigate what information is critical to convey to nurses when performing digital triage. In addition, the project aimed to investigate how such information could be visualized. This was done through a combined user research and machine learning approach, which enabled for a more nuanced and thorough investigation compared to only making use of one of the two fields. There is sparse research investigating how digital triaging can be improved and made more efficient. Therefore, this study has contributed with new and relevant insights. Three machine learning algorithms were implemented to predict the right level of care for a patient. Out of these three, the random forest classifier proved to have the best performance with an accuracy of 69.46%, also having the shortest execution time. Evaluating the random forest classifier, the most important features were stated to be the duration and progress of the symptoms, allergies to medicine, chronic diseases and the patient's own estimation of his/her health. These factors could all be confirmed by the user research approach, indicating that the results from the approaches were aligned. The results from the user research approach also showed that the patients' own description of their symptoms was of great importance. These findings served as a basis for a number of visualization decisions, aiming to make the triage process as accurate and efficient as possible.
49
Chen, Li. "Statistical Machine Learning for Multi-platform Biomedical Data Analysis." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77188.
Full textAbstract:
Recent advances in biotechnologies have enabled multiplatform and large-scale quantitative measurements of biomedical events. The need to analyze the produced vast amount of imaging and genomic data stimulates various novel applications of statistical machine learning methods in many areas of biomedical research. The main objective is to assist biomedical investigators to better interpret, analyze, and understand the biomedical questions based on the acquired data. Given the computational challenges imposed by these high-dimensional and complex data, machine learning research finds its new opportunities and roles. In this dissertation thesis, we propose to develop, test and apply novel statistical machine learning methods to analyze the data mainly acquired by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and single nucleotide polymorphism (SNP) microarrays. The research work focuses on: (1) tissue-specific compartmental analysis for dynamic contrast-enhanced MR imaging of complex tumors; (2) computational Analysis for detecting DNA SNP interactions in genome-wide association studies.
DCE-MRI provides a noninvasive method for evaluating tumor vasculature patterns based on contrast accumulation and washout. Compartmental analysis is a widely used mathematical tool to model dynamic imaging data and can provide accurate pharmacokinetics parameter estimates. However partial volume effect (PVE) existing in imaging data would have profound effect on the accuracy of pharmacokinetics studies. We therefore propose a convex analysis of mixtures (CAM) algorithm to explicitly eliminate PVE by expressing the kinetics in each pixel as a nonnegative combination of underlying compartments and subsequently identifying pure volume pixels at the corners of the clustered pixel time series scatter plot. The algorithm is supported by a series of newly proved theorems and additional noise filtering and normalization preprocessing. We demonstrate the principle and feasibility of the CAM approach together with compartmental modeling on realistic synthetic data, and compare the accuracy of parameter estimates obtained using CAM or other relevant techniques. Experimental results show a significant improvement in the accuracy of kinetic parameter estimation. We then apply the algorithm to real DCE-MRI data of breast cancer and observe improved pharmacokinetics parameter estimation that separates tumor tissue into sub-regions with differential tracer kinetics on a pixel-by-pixel basis and reveals biologically plausible tumor tissue heterogeneity patterns. This method has combined the advantages of multivariate clustering, convex optimization and compartmental modeling approaches.
Interactions among genetic loci are believed to play an important role in disease risk. Due to the huge dimension of SNP data (normally several millions in genome-wide association studies), the combinatorial search and statistical evaluation required to detect multi-locus interactions constitute a significantly challenging computational task. While many approaches have been proposed for detecting such interactions, their relative performance remains largely unclear, due to the fact that performance was evaluated on different data sources, using different performance measures, and under different experimental protocols. Given the importance of detecting gene-gene interactions, a thorough evaluation of the performance and limitations of available methods, a theoretical analysis of the interaction effect and the genetic factors it depends on, and the development of more efficient methods are warranted. Therefore, we perform a computational analysis for detect interactions among SNPs. The contributions are four-fold: (1) developed simulation tools for evaluating performance of any technique designed to detect interactions among genetic variants in case-control studies; (2) used these tools to compare performance of five popular SNP detection methods; and (3) derived analytic relationships between power and the genetic factors, which not only support the experimental results but also gives a quantitative linkage between interaction effect and these factors; (4) based on the novel insights gained by comparative and theoretical analysis, developed an efficient statistically-principled method, namely the hybrid correlation-based association (HCA) to detect interacting SNPs. The HCA algorithm is based on three correlation-based statistics, which are designed to measure the strength of multi-locus interaction with three different interaction types, covering a large portion of possible interactions. Moreover, to maximize the detection power (sensitivity) while suppressing false positive rate (or retaining moderate specificity), we also devised a strategy to hybridize these three statistics in a case-by-case way. A heuristic search strategy is also proposed to largely decrease the computational complexity, especially for high-order interaction detection. We have tested HCA in both simulation study and real disease study. HCA and the selected peer methods were compared on a large number of simulated datasets, each including multiple sets of interaction models. The assessment criteria included several power measures, family-wise type I error rate, and computational complexity. The experimental results of HCA on the simulation data indicate its promising performance in terms of a good balance between detection accuracy and computational complexity. By running on multiple real datasets, HCA also replicates plausible biomarkers reported in previous literatures.Ph. D.
50
Finch, Dezon K. "TagLine: Information Extraction for Semi-Structured Text Elements In Medical Progress Notes." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4321.
Full textAbstract:
Text analysis has become an important research activity in the Department of Veterans Affairs (VA). Statistical text mining and natural language processing have been shown to be very effective for extracting useful information from medical documents. However, neither of these techniques is effective at extracting the information stored in semi-structure text elements. A prototype system (TagLine) was developed as a method for extracting information from the semi-structured portions of text using machine learning. Features for the learning machine were suggested by prior work, as well as by examining the text, and selecting those attributes that help distinguish the various classes of text lines. The classes were derived empirically from the text and guided by an ontology developed by the Consortium for Health Informatics Research (CHIR), a nationwide research initiative focused on medical informatics. Decision trees and Levenshtein approximate string matching techniques were tested and compared on 5,055 unseen lines of text. The performance of the decision tree method was found to be superior to the fuzzy string match method on this task. Decision trees achieved an overall accuracy of 98.5 percent, while the string match method only achieved an accuracy of 87 percent. Overall, the results for line classification were very encouraging. The labels applied to the lines were used to evaluate TagLines' performance for identifying the semi-structures text elements, including tables, slots and fillers. Results for slots and fillers were impressive while the results for tables were also acceptable.