Дисертації з теми "Medical Machines"
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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.
Повний текст джерелаStadig 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.
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.
Повний текст джерелаIncludes 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.
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.
Повний текст джерелаMaster of Science
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.
Повний текст джерелаThis 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.
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.
Повний текст джерелаThe 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.
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.
Повний текст джерелаSmyth, Katherine Marie. "Piezoelectric micro-machined ultrasonic transducers for medical imaging." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108938.
Повний текст джерелаCataloged 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.
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.
Повний текст джерелаChi, Chih-Lin. "Medical decision support systems based on machine learning." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/283.
Повний текст джерелаLetzner, 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.
Повний текст джерелаBeslutet 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.
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.
Повний текст джерелаPunugu, Venkatapavani Pallavi. "Machine Learning in Neuroimaging." Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10284048.
Повний текст джерелаThe 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%.
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.
Повний текст джерелаFernandez, 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.
Повний текст джерелаPostovskaya, 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.
Повний текст джерелаAldosaky, 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.
Повний текст джерелаStrid, 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.
Повний текст джерелаFolk, 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.
Повний текст джерелаHjalmarsson, 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.
Повний текст джерелаBradley, Andrew Peter. "Machine learning for medical diagnostics: Techniques for feature extraction, classification, and evaluation." Thesis, University of Queensland, 1996.
Знайти повний текст джерелаBates, 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.
Повний текст джерелаFrunza, 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.
Повний текст джерелаDoğru, 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.
Повний текст джерелаLos 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
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.
Повний текст джерелаMachine 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
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.
Повний текст джерелаDetta 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.
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.
Повний текст джерелаmaking 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.
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.
Повний текст джерелаDeshpande, Hrishikesh. "Dictionary learning for pattern classification in medical imaging." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S032/document.
Повний текст джерелаMost 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
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.
Повний текст джерелаWallis, 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.
Повний текст джерелаWe 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
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.
Повний текст джерелаAlzubaidi, Laith. "Deep learning for medical imaging applications." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227812/1/Laith_Alzubaidi_Thesis.pdf.
Повний текст джерелаHavaei, Seyed Mohammad. "Machine learning methods for brain tumor segmentation." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10260.
Повний текст джерелаRésumé: 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.
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.
Повний текст джерелаThis 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
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.
Повний текст джерелаKanwal, Summrina. "Towards a novel medical diagnosis system for clinical decision support system applications." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/25397.
Повний текст джерелаKavanagh, 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/.
Повний текст джерелаDrusiani, Alberto. "Deep Learning Text Classification for Medical Diagnosis." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17281/.
Повний текст джерелаRahman, 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.
Повний текст джерелаFeng, 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.
Повний текст джерелаHartland, 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.
Повний текст джерелаNyongesa, 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.
Повний текст джерелаVerzellesi, 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/.
Повний текст джерелаKleine, Klaus. "Micromachining with single mode fibre lasers for medical device production." Thesis, University of Liverpool, 2009. http://livrepository.liverpool.ac.uk/1295/.
Повний текст джерелаLin, 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.
Повний текст джерелаMcGowan, 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.
Повний текст джерелаKabir, 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.
Повний текст джерелаAnsved, 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.
Повний текст джерелаChen, Li. "Statistical Machine Learning for Multi-platform Biomedical Data Analysis." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77188.
Повний текст джерелаPh. D.
Finch, Dezon K. "TagLine: Information Extraction for Semi-Structured Text Elements In Medical Progress Notes." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4321.
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