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Статті в журналах з теми "ML diagnostic model"
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Liu, Xinran, James Anstey, Ron Li, Chethan Sarabu, Reiri Sono, and Atul J. Butte. "Rethinking PICO in the Machine Learning Era: ML-PICO." Applied Clinical Informatics 12, no. 02 (March 2021): 407–16. http://dx.doi.org/10.1055/s-0041-1729752.
Повний текст джерелаАнотація:
Abstract Background Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.
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Wang, Dong, Jian Liu, Lijun Deng, and Honglin Wang. "Intelligent diagnosis of resistance variant multiple fault locations of mine ventilation system based on ML-KNN." PLOS ONE 17, no. 9 (September 30, 2022): e0275437. http://dx.doi.org/10.1371/journal.pone.0275437.
Повний текст джерелаАнотація:
The resistance variant faults (RVFs) observed in the mine ventilation system can utterly restrict mine safety production. Herein, a machine learning model, which is based on multi-label k-nearest neighbor (ML-KNN), is proposed to solve the problem of the rapid and accurate diagnosis of the RVFs that occur at multiple locations within the mine ventilation system. The air volume that passes through all the branches of the ventilation network, including the residual branches, was used as the diagnostic model input after the occurrence of multiple faults, whereas the label vector of the fault locations was used as the model’s output. In total, seven evaluation indicators and 1800 groups of randomly simulated faults at the typical locations in a production mine with 153 nodes and 223 branches were considered to evaluate the feasibility of the proposed model to solve for multiple fault locations diagnostic and verify the model’s generalization ability. After ten-fold cross-validation of the training sets containing 1600 groups of fault instances, the diagnostic accuracy of the model tested with the air volume of all 223 branches and the 71 residual branches’ air volume as input was 73.6% and 72.3%, respectively. On the other hand, To further evaluate the diagnostic performance of the model, 200 groups of the multiple fault instances that were not included in the training were tested. The accuracy of the fault location diagnosis was 76.5% and 73.5%, and the diagnostic time was 9.9s and 12.16s for the multiple faults instances with all 223 branches’ air volume and the 71 residual branches’ air volume as observation characteristics, respectively. The data show that the machine learning model based on ML-KNN shows good performance in the problem of resistance variant multiple fault locations diagnoses of the mine ventilation system, the multiple fault locations diagnoses can be carried out with all the branches’ air volume or the residual branches’ air volume as the input of the model, the diagnostic average accuracy is higher than 70%, and the average diagnosis time is less than one minute. Hence, the proposed model’s diagnostic accuracy and speed can meet the engineering requirements for the diagnosis of multiple fault locations for a real ventilation system in the field, and this model can effectively replace personnel to discover ventilation system failures, and also lays a good foundation for the construction of intelligent ventilation systems.
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Babar, Zaheer, Twan van Laarhoven, and Elena Marchiori. "Encoder-decoder models for chest X-ray report generation perform no better than unconditioned baselines." PLOS ONE 16, no. 11 (November 29, 2021): e0259639. http://dx.doi.org/10.1371/journal.pone.0259639.
Повний текст джерелаАнотація:
High quality radiology reporting of chest X-ray images is of core importance for high-quality patient diagnosis and care. Automatically generated reports can assist radiologists by reducing their workload and even may prevent errors. Machine Learning (ML) models for this task take an X-ray image as input and output a sequence of words. In this work, we show that ML models for this task based on the popular encoder-decoder approach, like ‘Show, Attend and Tell’ (SA&T) have similar or worse performance than models that do not use the input image, called unconditioned baseline. An unconditioned model achieved diagnostic accuracy of 0.91 on the IU chest X-ray dataset, and significantly outperformed SA&T (0.877) and other popular ML models (p-value < 0.001). This unconditioned model also outperformed SA&T and similar ML methods on the BLEU-4 and METEOR metrics. Also, an unconditioned version of SA&T obtained by permuting the reports generated from images of the test set, achieved diagnostic accuracy of 0.862, comparable to that of SA&T (p-value ≥ 0.05).
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Venkatesh, Veeramuthu, M. M. Anishin Raj, K. Mohamed Sajith, R. Anushiadevi, and T. Suriya Praba. "A precision-based diagnostic model ADOBE-accurate detection of breast cancer using logistic regression approach." Journal of Intelligent & Fuzzy Systems 39, no. 6 (December 4, 2020): 8419–26. http://dx.doi.org/10.3233/jifs-189160.
Повний текст джерелаАнотація:
Cancer is a prevalent disease which comes in several forms. The need of the hour in cancer research is to be able to diagnose cancer in its early stages. The furthermost common forms of cancer among women us breast cancer. In recent times, there has been a drastic increase in the number of breast cancer cases among women. As a wide range of medical data is available in electronic form and with easy access to Machine Learning(ML) techniques disease progression risk evaluation has been made easier. These ML tools can aid in giving us complex insights from the massive amounts of available data. Some of the techniques used for developing predictive models for perfect decision making in cancer research are Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs). Although it is acceptable that ML is used to predict cancer progression, we need some level of validation. In this paper, we have come up with a review of several ML methods in modelling cancer progression. We discuss several predictive models based on supervised ML techniques and the inputs given by users, along with the data available. The results that were obtained from Logistic Regression show us that this method gave a significantly higher accuracy than most other classifiers. The best accuracy is 98.2%, however, the best precision and recall is 100 and 98.60% correspondingly.
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Carreiro Pinasco, Gustavo, Eduardo Moreno Júdice de Mattos Farina, Fabiano Novaes Barcellos Filho, Willer França Fiorotti, Matheus Coradini Mariano Ferreira, Sheila Cristina de Souza Cruz, Andre Louzada Colodette, et al. "An interpretable machine learning model for covid-19 screening." Journal of Human Growth and Development 32, no. 2 (June 23, 2022): 268–74. http://dx.doi.org/10.36311/jhgd.v32.13324.
Повний текст джерелаАнотація:
Introduction: the Coronavirus Disease 2019 (COVID-19) is a viral disease which has been declared a pandemic by the WHO. Diagnostic tests are expensive and are not always available. Researches using machine learning (ML) approach for diagnosing SARS-CoV-2 infection have been proposed in the literature to reduce cost and allow better control of the pandemic. Objective: we aim to develop a machine learning model to predict if a patient has COVID-19 with epidemiological data and clinical features. Methods: we used six ML algorithms for COVID-19 screening through diagnostic prediction and did an interpretative analysis using SHAP models and feature importances. Results: our best model was XGBoost (XGB) which obtained an area under the ROC curve of 0.752, a sensitivity of 90%, a specificity of 40%, a positive predictive value (PPV) of 42.16%, and a negative predictive value (NPV) of 91.0%. The best predictors were fever, cough, history of international travel less than 14 days ago, male gender, and nasal congestion, respectively. Conclusion: We conclude that ML is an important tool for screening with high sensitivity, compared to rapid tests, and can be used to empower clinical precision in COVID-19, a disease in which symptoms are very unspecific.
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Smirnova, Darya Ilyinichna, Anastasiya Vyacheslavovna Gracheva, Elena Aleksandrovna Volynskaya, Vitaliy Vasilievich Zverev, and Evgeniy Bakhtiyorovich Faizuloev. "Diagnostic value of the LAMP method with real-time fluo-rescence detection on a model of herpesvirus infection." Sanitarnyj vrač (Sanitary Doctor), no. 1 (January 1, 2021): 52–61. http://dx.doi.org/10.33920/med-08-2101-06.
Повний текст джерелаАнотація:
Due to the high prevalence and clinical significance of herpesvirus diseases, improvement of methods of their diagnostic remains relevant.The aim of the work: improvement of the methodological approaches to the rapid differential diagnosis of herpesvirus infections based on the LAMP method with real-time fluorescence detection (LAMPRT). 201 urogenital swabs were examined using RT-PCR and LAMP-RT, 27of which contained DNAof Epstein-Barr virus (EBV), 34 — Cytomegalovirus (CMV), 14 — Herpes simplex virus type 1 (HSV-1), 36 — Herpes simplex virus type 2 (HSV-2). For LAMP-RT reaction we used Bst 2.0 WarmStart DNA polymerase (BioLabs, Great Britain), SYTO-82 dye (Invitrogen, USA), and sets of primers for herpesvirus DNA detection by LAMP. High efficiency of using the SYTO-82 dye for the detection of herpesvirus DNA in the LAMP-RT reactionwas shown. Under the optimal conditions, the LAMP-RT allows to reduce the reaction time to 25 minutes, that 3 times less then real-time PCR. The diagnostic sensitivity of the LAMP-RT reaction for HSV-1 was 100 %, HSV-2 — 94.50 %, EBV — 89 %, CMV — 94 %. The diagnostic specificity for all studied viruses was 100 %. The analytical sensitivity of EBV detection was 104 DNA copies/ ml, for HSV types 1 and 2 and CMV — 103 DNA copies/ml. Thus, the LAMP-RT reaction with SYTO-82 dye makes it possible to detect the DNA of various herpesviruses in clinical specimens with high sensitivity and specificity and can be considered as a promising method for point-of-care diagnostics. For the widespread implementation of the method into the practice of laboratory diagnostics, it is necessary to solve the problem of creating an internal positive control of the reaction and the development of portable specialized analyzers.
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Baker, Mohammed Rashad, D. Lakshmi Padmaja, R. Puviarasi, Suman Mann, Jeidy Panduro-Ramirez, Mohit Tiwari, and Issah Abubakari Samori. "Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM)." Computational and Mathematical Methods in Medicine 2022 (April 14, 2022): 1–12. http://dx.doi.org/10.1155/2022/6501975.
Повний текст джерелаАнотація:
Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments where complex neuroimages are discriminated using ML technology. Radiologists and researchers found that sole decision by the ML algorithms is not accurate enough to implement the treatment procedure. Thus, an intelligent decision is required further by the radiologists after evaluating the ML outcomes. The current research is based on the critical ML, where radiologists’ critical thinking ability, IQ (intelligence quotient), and experience in radiology have been examined to understand how these factors affect the accuracy of neuroimaging discrimination. A primary quantitative survey has been carried out, and the data were analysed in IBM SPSS. The results showed that experience in works has a positive impact on neuroimaging discrimination accuracy. IQ and trained ML are also responsible for improving the accuracy as well. Thus, radiologists with more experience in that field are able to improve the discriminative and diagnostic capability of CML.
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Chatterjee, S., R. Alkhaldi, P. Yaadav, D. Bethineedi, A. Shreya, and N. Bankole. "P.115 Diagnostic performance of machine learning based MR algorithm vs conventional MR images for predicting the likelihood of brain tumors." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 49, s1 (June 2022): S38. http://dx.doi.org/10.1017/cjn.2022.207.
Повний текст джерелаАнотація:
Background: MRI forms an imperative part of the diagnostic and treatment protocol for primary brain tumors and metastasis. Though conventional T1W MRI forms the basis for diagnosis at present, it faces several limitations. Machine learning (ML) algorithms require less expertise and provide better diagnostic accuracy. Methods: A systematic review of PubMed, Google Scholar, and Cochrane databases along with registries through 1980-2021 was done. Original articles in English evaluating Conventional MRI or ML algorithms. Data was extracted by 2 reviewers and meta-analysis was performed using bivariate regression model. Results: The study protocol was registered under PROSPERO. Twelve studies with 1247 participants were included for systematic analysis and three studies for meta-analysis. ML algorithms had better aggregate sensitivity and specificity (80%, 83.14%) than Conventional MRI (81.84%, 74.78%).The pooled sensitivity, specificity, DOR for the studies were 0.926 (95% CI, 0.840-0.926), 0.991 (95% CI, 0.955-0.998) and 1446.946 (312.634-6692.646) with AUC=0.904 under HSROC. On subgroup analysis, MRS and Random Forest Model had highest sensitivity and specificity (100%,100%;100%,100%), DSC MRI and Deep Neural Network had highest AUC (0.98,0.986). Conclusions: ML algorithm has superior diagnostic performance and faster diagnostic capability once trained than conventional imaging for brain tumors. It has immense potential to be the standard of care in the future.
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Kahlen, Jannis N., Michael Andres, and Albert Moser. "Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault." Energies 14, no. 20 (October 18, 2021): 6816. http://dx.doi.org/10.3390/en14206816.
Повний текст джерелаАнотація:
Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However, the performance of the diagnostic systems depends on the quantity and quality of the data. The training database can be augmented utilizing data augmentation techniques that generate synthetic data to improve diagnostic performance. However, existing data augmentation techniques are generic methods that do not include additional information in the synthetic data. In this paper, we develop a model-based data augmentation technique integrating computer-implementable electromechanical models. Synthetic normal- and abnormal-condition data are generated with an electromechanical model and a stochastic parameter value sampling method. The model-based data augmentation is showcased to detect an abnormal condition of a distribution transformer. First, the synthetic data are compared with the measurements to verify the synthetic data. Then, ML-based diagnostic systems are created using model-based data augmentation and are compared with state-of-the-art diagnostic systems. It is shown that using the model-based data augmentation results in an improved accuracy compared to state-of-the-art diagnostic systems. This holds especially true when only a small abnormal-condition database is available.
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Gui, Chloe, and Victoria Chan. "Machine learning in medicine." University of Western Ontario Medical Journal 86, no. 2 (December 3, 2017): 76–78. http://dx.doi.org/10.5206/uwomj.v86i2.2060.
Повний текст джерелаАнотація:
Machine learning (ML) is a powerful and flexible tool that can be used to analyze and predict outcomes from biological and clinical data. ML models have the potential to improve healthcare efficiency in a number of ways. Algorithms that predict prognosis empower healthcare officials to allocate resources optimally and physicians to select better treatment options for patients. Diagnostic models can be used in screening, in risk stratification, and to recommend appropriate testing and treatment. This would decrease the burden on physicians, increase and expedite patient access to care, save resources, and reduce costs. However, despite the research advances of ML in medicine, its role in the clinic is currently limited. Model building and validation may require large amounts of high-quality data that can be difficult and expensive to obtain, and diagnostic models must be individually built for each disease, a lengthy process. The psychological aspect of trusting black box algorithms may also be challenging to accept. Continued ML research, however, may enable the use of smaller datasets and the development of more transparent models. Careful trials in the clinic will need to be conducted before the more impactful uses of ML, such as diagnosis, can be implemented.
Дисертації з теми "ML diagnostic model"
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Navicelli, Andrea, Mario Tucci, and Filippo De Carlo. "Analisi ed applicazione di modelli diagnostici e prognostici per guasti e prestazioni di componenti di impianti industriali nell’era I4.0." Doctoral thesis, 2021. http://hdl.handle.net/2158/1234822.
Повний текст джерелаАнотація:
Il ruolo fondamentale che la manutenzione gioca nei costi di esercizio e nella produttività degli impianti industriali ha portato le aziende e i ricercatori a spostare il loro interesse su questo tema. L'ultima frontiera dell'innovazione in campo manutentivo, resa possibile anche dall'avvento della quarta rivoluzione industriale che promuove la sensorizzazione e l’interconnessione di tutti i macchinari di impianto, è la manutenzione predittiva. Essa mira ad ottenere una previsione accurata della vita utile dei componenti degli impianti industriali al fine di ottimizzare la schedulazione degli interventi sul campo.
Lo studio parte da una accurata revisione della letteratura scientifica di settore riguardante le tecniche diagnostiche e prognostiche applicate a componenti di impianti industriali, necessaria alla comprensione dei diversi modelli sviluppati in funzione della tipologia di componente e modo di guasto in analisi. Successivamente ho spostato l’attenzione sul concetto di manutenzione 4.0 al fine di mappare tutte le caratteristiche associate al paradigma dell'Industria 4.0 e le loro possibili applicazioni alla manutenzione.
Lo studio condotto ha portato poi alla progettazione, sviluppo e validazione delle metodologie necessarie all’applicazione in real-time di modelli diagnostici e prognostici avanzati, sia statistici che machine learning, necessari all’implementazione sul campo di un sistema di manutenzione predittiva.
Grazie all’applicazione delle metodologie proposte ad un caso studio è stato possibile non solo validare i modelli proposti ma anche definire l’architettura informatica necessaria alla loro corretta implementazione sul sistema distribuito di controllo (Distributed Control System - DCS) di impianto in funzione della tipologia del componente e del guasto in analisi.
I modelli testati e validati hanno mostrato elevate prestazioni diagnostiche soprattutto per quanto riguarda i modelli ML che sfruttano le Support Vector Machine (SVM).
In definitiva, questo lavoro di tesi mostra nel dettaglio tutti i passaggi necessari allo sviluppo di un sistema di manutenzione predittiva efficace in impianto: partendo dall’analisi dei modi di guasto e dalla sensorizzazione dei componenti, passando poi allo sviluppo dei modelli diagnostici e prognostici real-time fino alla costruzione dell’interfaccia di visualizzazione dei risultati delle analisi svolte, analizzando anche l’architettura informatica necessaria al suo corretto funzionamento.
The fundamental role that maintenance plays in the operating costs and productivity of industrial plants has led companies and researchers to shift their interest in this issue. The last frontier of innovation in the maintenance field, made possible also by the advent of the fourth industrial revolution which promotes the sensorisation and interconnection of all plant machinery, is predictive maintenance. It aims to obtain an accurate forecast of the useful life of the industrial plants’ components in order to optimise the scheduling of interventions in the field.
The study starts from an accurate review of the scientific literature concerning the diagnostic and prognostic techniques applied to industrial plant components, necessary to understand the different models developed according to the type of component and failure mode under analysis. Subsequently I shifted the focus to the maintenance 4.0 concept in order to map all the characteristics associated with the Industry 4.0 paradigm and their possible applications to maintenance operations.
The study then led to the design, development and validation of the methodologies necessary for the real-time application of advanced diagnostic and prognostic models, both statistical and machine learning, necessary for the field implementation of a predictive maintenance system.
Thanks to the application of the proposed methodologies to a case study, it was possible not only to validate the proposed models but also to define the IT architecture necessary for their correct implementation on the plant's Distributed Control System (DCS) according to the type of component and the fault under analysis.
The tested and validated models showed high diagnostic performance, especially regarding the Support Vector Machine (SVM) Machine Learning models.
Ultimately, this thesis shows in detail all the steps necessary for the development of an effective predictive maintenance system in the plant: starting from the analysis of failure modes and component sensorisation, then moving on to the development of real-time diagnostic and prognostic models up to the build-up of the interface for visualising the results of the analyses carried out, also analysing the IT architecture necessary for its correct operation.
Частини книг з теми "ML diagnostic model"
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Aria, Massimo, Corrado Cuccurullo, and Agostino Gnasso. "Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests." In Proceedings e report, 179–84. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.34.
Повний текст джерелаАнотація:
The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields, especially the healthcare context. However, it still has limitations and drawbacks, such as the lack of interpretability which does not allow users to understand how certain decisions are made. This drawback is identified with the term "Black-Box", as well as models that do not allow to interpret the internal work of certain ML techniques, thus discouraging their use. In a highly regulated and risk-averse context such as healthcare, although "trust" is not synonymous with decision and adoption, trusting an ML model is essential for its adoption. Many clinicians and health researchers feel uncomfortable with black box ML models, even if they achieve high degrees of diagnostic or prognostic accuracy. Therefore more and more research is being conducted on the functioning of these models. Our study focuses on the Random Forest (RF) model. It is one of the most performing and used methodologies in the context of ML approaches, in all fields of research from hard sciences to humanities. In the health context and in the evaluation of health policies, their use is limited by the impossibility of obtaining an interpretation of the causal links between predictors and response. This explains why we need to develop new techniques, tools, and approaches for reconstructing the causal relationships and interactions between predictors and response used in a RF model. Our research aims to perform a machine learning experiment on several medical datasets through a comparison between two methodologies, which are inTrees and NodeHarvest. They are the main approaches in the rules extraction framework. The contribution of our study is to identify, among the approaches to rule extraction, the best proposal for suggesting the appropriate choice to decision-makers in the health domain.
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Morande, Swapnil, Veena Tewari, and Kanwal Gul. "Reinforcing Positive Cognitive States with Machine Learning: An Experimental Modeling for Preventive Healthcare." In Healthcare Access - New Threats, New Approaches [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108272.
Повний текст джерелаАнотація:
Societal evolution has resulted in a complex lifestyle where we give most attention to our physical health leaving psychological health less prioritized. Considering the complex relationship between stress and psychological well-being, this study bases itself on the cognitive states experienced by us. The presented research offers insight into how state-of-the-art technologies can be used to support positive cognitive states. It makes use of the brain-computer interface (BCI) that drives the data collection using electroencephalography (EEG). The study leverages data science to devise machine learning (ML) model to predict the corresponding stress levels of an individual. A feedback loop using “Self Quantification” and “Nudging” offer real-time insights about an individual. Such a mechanism can also support the psychological conditioning of an individual where it does not only offer spatial flexibility and cognitive assistance but also results in enhanced self-efficacy. Being part of quantified self-movement, such an experimental approach could showcase personalized indicators to reflect a positive cognitive state. Although ML modeling in such a data-driven approach might experience reduced diagnostic sensitivity and suffer from observer variability, it can complement psychosomatic treatments for preventive healthcare.
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Naidenova, Xenia. "Machine Learning as a Commonsense Reasoning Process." In Handbook of Research on Innovations in Database Technologies and Applications, 605–11. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-242-8.ch065.
Повний текст джерелаАнотація:
One of the most important tasks in database technology is to combine the following activities: data mining or inferring knowledge from data and query processing or reasoning on acquired knowledge. The solution of this task requires a logical language with unified syntax and semantics for integrating deductive (using knowledge) and inductive (acquiring knowledge) reasoning. In this paper, we propose a unified model of commonsense reasoning. We also demonstrate that a large class of inductive machine learning (ML) algorithms can be transformed into the commonsense reasoning processes based on wellknown deduction and induction logical rules. The concept of a good classification (diagnostic) test (Naidenova & Polegaeva, 1986) is the basis of our approach to combining deductive and inductive reasoning.
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Naidenova, Xenia. "Machine Learning as a Commonsense Reasoning Process." In Machine Learning, 113–19. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch201.
Повний текст джерелаАнотація:
One of the most important tasks in database technology is to combine the following activities: data mining or inferring knowledge from data and query processing or reasoning on acquired knowledge. The solution of this task requires a logical language with unified syntax and semantics for integrating deductive (using knowledge) and inductive (acquiring knowledge) reasoning. In this paper, we propose a unified model of commonsense reasoning. We also demonstrate that a large class of inductive machine learning (ML) algorithms can be transformed into the commonsense reasoning processes based on wellknown deduction and induction logical rules. The concept of a good classification (diagnostic) test (Naidenova & Polegaeva, 1986) is the basis of our approach to combining deductive and inductive reasoning.
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Mustary, Nareshkumar, and Phani Kumar Singamsetty. "Prediction and Recommendation System for Diabetes Using Machine Learning Models." In Advances in Healthcare Information Systems and Administration, 316–27. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7709-7.ch018.
Повний текст джерелаАнотація:
Diabetes is one of the most deadly diseases on the planet. It is also a cause of a variety of illnesses, such as coronary artery disease, blindness, and urinary organ disease. In this situation, the patient must visit a medical center to obtain their results following consultation. Finding the right combination of characteristics and machine learning techniques for classification is also very critical. However, with the advancement of machine learning techniques, we now have the potential to find a solution to the current problem. The healthcare recommendation system (HRS) may be designed to predict health by evaluating patient lifestyle, physical health, mental health aspects using machine learning. For example, training the model using people's age and diabetes helps to predict new patients without a specific diagnostic for diabetes. The proposed deep learning model with convolutional neural network (D-CNN) achieves an overall accuracy of 96.25%. D-CNN is found to be more successful for diabetes prediction than other machine learning (ML) approaches in the experimental analysis.
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J. Kleczyk, Ewa, Tarachand Yadav, and Stalin Amirtharaj. "Applying Machine Learning Algorithms to Predict Endometriosis Onset." In Endometriosis - Recent Advances, New Perspectives and Treatments [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.101391.
Повний текст джерелаАнотація:
Endometriosis is a commonly occurring progressive gynecological disorder, in which tissues similar to the lining of the uterus grow on other parts of the female body, including ovaries, fallopian tubes, and bowel. It is one of the primary causes of pelvic discomfort and fertility challenges in women. The actual cause of the endometriosis is still undetermined. As a result, the objective of the chapter is to identify the drivers of endometriosis’ diagnoses via leveraging selected advanced machine learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a greater extent if a likelihood of endometriosis could be predicted well in advance. Logistic regression (LR) and eXtreme Gradient Boosting (XGB) algorithms leveraged 36 months of medical history data to demonstrate the feasibility. Several direct and indirect features were identified as important to an accurate prediction of the condition onset, including selected diagnosis and procedure codes. Creating analytical tools based on the model results that could be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers might aid the objective of improving the diagnostic processes and result in a timely and precise diagnosis, ultimately increasing patient care and quality of life.
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Jagota, Vishal, Vinay Bhatia, Luis Vives, and Arun B. Prasad. "ML-PASD." In Advances in Medical Diagnosis, Treatment, and Care, 82–93. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7460-7.ch006.
Повний текст джерелаАнотація:
Autism spectrum disorder (ASD) is growing faster than ever before. Autism detection is costly and time intensive with screening procedures. Autism can be detected at an early stage by the development of artificial intelligence and machine learning (ML). While a number of experiments using many approaches were conducted, these studies provided no conclusion as to the prediction of autism characteristics in various age groups. This chapter is therefore intended to suggest an accurate MLASD predictive model based on the ML methodology to prevent ASD for people of all ages. It is a method for prediction. This survey was conducted to develop and assess ASD prediction in an artificial neural network (ANN). AQ-10 data collection was used to test the proposed pattern. The findings of the evaluation reveal that the proposed prediction model has improved results in terms of consistency, specificity, sensitivity, and dataset accuracy.
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Pandey, Upasana, Tejveer Shakya, Meet Rajput, Rakshit Singh, and Tanish Mangal. "Review and Analysis of Disease Diagnostic Models Using AI and ML." In Advances in Medical Technologies and Clinical Practice, 35–53. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6957-6.ch003.
Повний текст джерелаАнотація:
Recently, disease prediction using diagnostic reports and images are one of the most popular applications of artificial intelligence (AI) and machine learning (ML). Several authors reported significant results in this area by combining cutting-edge hardware with AI and ML-based technologies. In this chapter, the authors present a review of different works carried for the prediction of several chronic diseases by researchers in last five years. Reported AI and ML based methodologies have been used to forecast chronic disease such as heart problems, brain tumors, asthma, diabetes, cholera, arthritis, liver diseases, kidney diseases, malaria, and leukemia. In the literature, the authors also discuss the different user interfaces which have been used to interact with real time AI and ML based disease prediction models. The authors have presented the detailed discussion of each paper including advantages, disadvantages, datasets, performance metrics such as precision, recall, accuracy and F1 score. In the final section, the survey concludes with a description of research gaps that can be addressed by future research attempts.
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R., Maheswari, Pattabiraman Venkatasubbu, and A. Saleem Raja. "Gait Analysis Using Principal Component Analysis and Long Short Term Memory Models." In Structural and Functional Aspects of Biocomputing Systems for Data Processing, 79–97. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6523-3.ch004.
Повний текст джерелаАнотація:
Human analysis and diagnosis have become attractive technology in many fields. Gait defines the style of movement and gait analysis is a study of human activity to inspect the style of movement and related factors used in the field of biometrics, observation, diagnosis of gait disease, treatment, rehabilitation, etc. This work aims in providing the benefit of analysis of gait with different sensors, ML models, and also LSTM recurrent neural network, using the latest trends. Placing the sensors at the proper location and measuring the values using 3D axes for these sensors provides very appropriate results. With proper fine-tuning of ML models and the LSTM recurrent neural network, it has been observed that every model has an accuracy of greater than 90%, concluding that LSTM performance is observed to be slightly higher than machine learning models. The models helped in diagnosing the disease in the foot (if there is injury in the foot) with high efficiency and accuracy. The key features are proven to be available and extracted to fit the LSTM RNN model and have a positive outcome.
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Refaee, Mahmoud A., Hamada R. H. Al-Absi, Mohammad Tariqul Islam, Mowafa Househ, Zubair Shah, M. Sohel Rahman, and Tanvir Alam. "The Linkage Between Bone Densitometry and Cardiovascular Disease." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti210905.
Повний текст джерелаАнотація:
Dual-energy X-ray absorptiometry (DXA) has been traditionally used to assess body composition covering bone, fat and muscle content. Cardiovascular disease (CVD) has deleterious effects on bone health and fat composition. Therefore, early detection of bone health, fat and muscle composition would help to anticipate a proper diagnosis and treatment plan for CVD patients. In this study, we leveraged machine learning (ML)-based models to predict CVD using DXA, demonstrating that it can be considered an innovative approach for early detection of CVD. We leveraged state-of-the-art ML models to classify the CVD group from non-CVD group. The proposed logistic regression-based model achieved nearly 80% accuracy. Overall, the bone mineral density, fat content, muscle mass and bone surface area measurements were elevated in the CVD group compared to non-CVD group. Ablation study revealed a more successful discriminatory power of fat content and bone mineral density than muscle mass and bone areas. To the best of our knowledge, this work is the first ML model to reveal the association between DXA measurements and CVD in the Qatari population. We believe this study will open new avenues of introducing DXA in creating the diagnosis and treatment plan of cardiovascular diseases.
Тези доповідей конференцій з теми "ML diagnostic model"
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Singh, Ajay, Anand Shukla, and Suryansh Purwar. "Leveraging Machine Learning and Interactive Voice Interface for Automated Production Monitoring and Diagnostic." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210475-ms.
Повний текст джерелаАнотація:
Abstract Automated production monitoring and diagnostics is becoming essential for oil producers to achieve operational efficiency. In this work a combination of unsupervised and supervised machine-learning (ML) models are proposed and were integrated with interactive voice interface so that production diagnostic reports can be generated by using interactive session with chatbot. To achieve this, current work proposes an integration of ML models and chatbot in the cloud native environment and presents a case study using data from hundreds of wells supported on plunger lift system. Within ML framework data preprocessing and principle component analysis (PCA) was performed. The purpose of PCA was to identify principle components (PCs) and the projection production rate data over few dominating PCs and generate 2D or 3D plots which can be used to cluster wells based on production trends and relative performance. Then using daily production data, a regression tree analysis (per well) was performed to predict production rate for dominating phase for production. Regression tree generated if-else type rules which were used for production diagnostics. Further, using early few months of time series data for production, pressure and artificial lift data, another PCA model was trained and contribution chart (per well) were developed to identify which are the most contributing variables towards the change in the production such as increase or decrease in production rate. Finally, to enhance end user experience, a cloud native chatbot leveraging cloud services was configured to perform all steps involved in ML framework in serverless compute environment. The chatbot was built to answer frequently asked production monitoring and diagnostics questions such as "provide me a list of poor performing well" etc. The proposed framework was applied to wells supported on plunger lift and PCA revealed that that four PCs were enough to capture most dominating production modes and first 3 PC described 96.2% of variance. The diagnostic charts were built utilizing 2D and 3D diagrams using projection of gas production rate over first 3 PCs. This was found visually extremely useful to identify which well or group of wells were not performing as expected when compared to rest of the wells. Just by looking 2D plot about 10% wells were found with significant decrease while about 15% were found moderate decrease in production rate. Once identified poorly performing wells regression tree analysis was automatically generated along with the contribution charts for all variables. Couple of case studies were presented using two different wells with contrast production trend and it was demonstrated that the present workflow was able to identify relative behavior of those wells and presented detailed diagnostics using regression tree analysis and contribution charts. Overall, diagnostic charts were able to identify how to calibrate plunger count, plunger velocity, trip time etc. for improved production and forecasted up to 30% production improvement for poor producing wells. Finally, the results were tested out with chatbot. The chatbot model was deployed using web user interface and to answer production diagnostics related questions, chatbot utilized serverless compute to run ML models on the cloud. The output such as generated diagnostic charts and list of well etc. were prepared as user asked the questions and relevant analysis was presented to end user within a fraction of second. This can reduce time taken by well diagnostic analysis by 80%
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Jaw, Link C., and Yuh-Jye Lee. "Engine Diagnostics in the Eyes of Machine Learning." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27088.
Повний текст джерелаАнотація:
Machine learning (ML) is a data-driven approach to discovering patterns and knowledge, and it is different from the physics-based approach, which uses the principles of physics to describe a phenomenon. Physics-based approach has dominated the field of engine diagnostics because of the maturity of scientific and engineering knowledge embodied in the design and manufacturing of the engine and its components. Nevertheless, development of ML techniques has accelerated in the last three decades, and the techniques can potentially lower development time and are applicable to a wide variety of industries. This paper examines some of the most commonly cited ML techniques for handling numerical data and applies them to a gas turbine engine diagnostic problem. The diagnostic problem is to isolate the symptom of engine performance degradations to a root-cause fault or failure. This fault isolation problem is a type of classification problem in the ML world. A hypothetical engine model for commercial airplanes is used in simulation to create a standard dataset. This dataset is then used by all of the selected techniques. The results from the ML algorithms are evaluated in terms of classification accuracy and misclassification rates.
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Xie, Jiarui, Katherine Schmidt, Nausica Budeanu, Vincent Letendre, and Yaoyao Fiona Zhao. "Combining Feature Learning and Transfer Learning in Balancing Anomaly Detection for Gas Turbine Engine Vibration Analysis." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-88223.
Повний текст джерелаАнотація:
Abstract Rotor imbalance is a vital measure that indicates the health state of a gas turbine (GT). Abnormal balancing patterns will lead to excessive vibration and gradually compromise structural integrity. This paper presents the construction of anomaly detection (AD) models that recognize abnormal balancing patterns for two aeroderivative GTs, AGT-A and AGT-B, from Siemens Energy. Such a diagnostic tool can predict at an early stage whether a high vibration would occur during the vibration test and avoid engine reject for re-balance. Machine learning (ML) algorithms have been extensively utilized to conduct GT design space exploration and condition monitoring. However, ML has not been implemented to improve the efficiency of GT manufacturing processes, mainly due to data scarcity. The authors propose a combined feature learning and transfer learning technique to leverage the data resources of GT manufacturing processes. The physical and operational similarities between GTs belonging to the same series imply the transferability of features between models. The normal balancing patterns of the data-rich AGT-A were first learned by a sparse autoencoder to detect balancing anomalies. Then, the learned features were used to initialize the balancing AD model for the data-poor AGT-B. The test accuracy of the AGT-B AD model was increased from 75% to 92% with transfer learning. The presented methodology can facilitate and enable various data-driven analysis tasks for the manufacturing processes of original equipment manufacturers.
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Khan, Abdul Muqtadir, Abdullah BinZiad, Abdullah Al Subaii, Denis Bannikov, Maksim Ponomarev, and Sergey Parkhonyuk. "Fracture Height Prediction Model Utilizing Openhole Logs, Mechanical Models, and Temperature Cooldown Analysis with Machine Learning Algorithms." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207975-ms.
Повний текст джерелаАнотація:
Abstract Vertical wells require diagnostic techniques after minifrac pumping to interpret fracture height growth. This interpretation provides vital input to hydraulic fracturing redesign workflows. The temperature log is the most widely used technique to determine fracture height through cooldown analysis. A data science approach is proposed to leverage available measurements, automate the interpretation process, and enhance operational efficiency while keeping confidence in the fracturing design. Data from 55 wells were ingested to establish proof of concept.The selected geomechanical rock texture parameters were based on the fracturing theory of net-pressure-controlled height growth. Interpreted fracture height from input temperature cooldown analysis was merged with the structured dataset. The dataset was constructed at a high vertical depth of resolution of 0.5 to 1 ft. Openhole log data such as gamma-ray and bulk density helped to characterize the rock type, and calculated mechanical properties from acoustic logs such as in-situ stress and Young's modulus characterize the fracture geometry development. Moreover, injection rate, volume, and net pressure during the calibration treatment affect the fracture height growth. A machine learning (ML) workflow was applied to multiple openhole log parameters, which were integrated with minifrac calibration parameters along with the varying depth of the reservoir. The 55 wells datasets with a cumulative 120,000 rows were divided into training and testing with a ratio of 80:20. A comparative algorithm study was conducted on the test set with nine algorithms, and CatBoost showed the best results with an RMSE of 4.13 followed by Random Forest with 4.25. CatBoost models utilize both categorical and numerical data. Stress, gamma-ray, and bulk density parameters affected the fracture height analyzed from the post-fracturing temperature logs. Following successful implementation in the pilot phase, the model can be extended to horizontal wells to validate predictions from commercial simulators where stress calculations were unreliable or where stress did not entirely reflect changes in rock type. By coupling the geometry measurement technology with data analysis, a useful automated model was successfully developed to enhance operational efficiency without compromising any part of the workflow. The advanced algorithm can be used in any field where precise fracture placement of a hydraulic fracture contributes directly to production potential. Also, the model can play a critical role in cube development to optimize lateral landing and lateral density for exploration fields.
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Kayode, Babatope O., Karl D. Stephen, and Abdullah Kaba. "Application of Data Science Algorithms to Establish a Novel Parameterization Approach for Static and Dynamic Models." In SPE Symposium: Leveraging Artificial Intelligence to Shape the Future of the Energy Industry. SPE, 2023. http://dx.doi.org/10.2118/214476-ms.
Повний текст джерелаАнотація:
Abstract Numerical simulation results are the basis of numerous oil and gas field developments. We based the numerical simulation models (or dynamic models) on 3D geological models. We constructed a geological model using core and log data obtained from wells as inputs to create a reservoir prototype. This paper describes the applications of artificial intelligence (AI) algorithms for parameterization of static and dynamic modeling processes. Accordingly, a hypothetical 3D geological model was created, and porosity and permeability were distributed using sequential Gaussian simulation. Then, Petro-physical rock types (PRT) were defined in the 3D space as a function of porosity and permeability using a hypothetical Winland's R35 equation. Finally, hypothetical saturation-height functions (SHFs) were defined for different PRTs to populate water saturation in the 3D geological model. Subsequently, some wells were randomly defined in the 3D model to obtain the logs of porosity, permeability, SHF, PRT, repeat formation tester pressure (RFT), and datum pressures that are used in this study. A multivariate Gaussian regression was applied for anomaly detection, while core porosity and permeability were filtered. Subsequently, a fixed window average was used to detect the boundaries of core data stationarity and propose the optimum reservoir zone required to describe the internal heterogeneities of the reservoir. Then, we deployed the k-means clustering algorithm to determine the PRT and saturation height function (SHF) based on the core and log data derived from the hypothetical geological model. Finally, we used the clustering-based pattern recognition to cluster well datum pressures into homogeneous groups and create a connected reservoir region CRR map to be used as an input in the 3D permeability distribution. Our results demonstrate the value of additional diagnostics that can be used in conjunction with the traditional semi-log plot of porosity and permeability. This additional diagnostic approach is a semi-log plot of permeability versus depth, which can help check whether intra-reservoir heterogeneities observable in core data have been preserved in the 3D model. In our case, a 3D model created using the core and log data from the hypothetical model and honoring the internal reservoir architecture resulted in a better history match regarding the hypothetical geo-model's RFT pressure signature. Our results further demonstrate that PRT and SHF derived from k-means clustering are sufficiently similar to those of the hypothetical model. Time series anomaly filtering of pressures helped detect incorrect well data that may otherwise have gone unnoticed. Using the nearest-neighbor property distribution resulted in a geological model whose diagnostic plots indicated an excellent match with core data and allowed a better assessment of modeling uncertainties. The ML approaches presented in this study could help obtain data-derived PRT and SHF to complement Winland's interpretation when Mercury Injection Capillary Pressure (MICP) experiments are limited or unavailable, saving both time and cost. Using the fixed window averaging helps optimize the geological model zone assessment, resulting in a better intra-reservoir architecture. Finally, we derive insights into a more efficient core acquisition plan.
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Borsukov, A. V., S. B. Krukovskiy, L. N. Markelova, O. A. Gorbatenko, and D. Yu Venidiktova. "Contrast-enhanced ultrasound of kidneys in patients with type 2 diabetes and chronic pyelonephritis: a new dosage of the contrast-enhanced agent." In Наука России: Цели и задачи. НЦ "LJournal", 2021. http://dx.doi.org/10.18411/sr-10-04-2021-65.
Повний текст джерелаАнотація:
Objective. To evaluate the diagnostic efficacy of the contrast-enhanced ultrasound examination of kidneys in patients with chronic pyelonephritis with a dose of injected contrast agent – 1.0 ml. Materials and methods. In 2020, 20 patients with chronic pyelonephritis were examined on the basis of the Fundamental research laboratory “Diagnostic Researches and Minimally Invasive Technologies”, Smolensk State Medical University. All patients underwent ultrasound examination Doppler mapping mode of the kidneys and the. Also, all patients underwent contrast-enhanced ultrasound examination of the kidneys for the diagnosis of angionephrosclerosis. Results. Using the improved technique in patients of group 2 compared with patients in group 1, the quality of the images obtained was preserved. In patients of group 1 with chronic pyelonephritis, the quantitative indicators correspond to the initial manifestations of angionephrosclerosis. Conclusion. Thus, the improved CEUS technique with the use of 1.0 ml of contrast agent showed good possibilities in the diagnosis of angionephrosclerosis in patients with chronic pyelonephritis.
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Duvvuri, Anishka, Navya Kovvuri, Sneka Kumar, Rebecca Victor, and Tanush Kaushik. "Comparative Study of Anxiety Symptom’s Predictions From Discord Chat Messages using Automl." In 4th International Conference on Machine Learning and Soft Computing. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130202.
Повний текст джерелаАнотація:
Anxiety is a chronic illness especially during the Covid and post-pandemic era. It’s important to diagnose anxiety in its early stages. Traditional Machine learning (ML) methods have been developmental intense procedures to detect mental health issues, but Automated machine learning (AutoML) is a method whereby the novice user can build a model to detect a phenomenon such as Generalized Anxiety Disorder (GAD) fairly easily. In this study we evaluate a popular AutoML technique with recent chat engine (Discord) conversation dataset using anxiety hashtags. This multi-symptom AutoML Random Forest predictive model is at least 75+% accurate with the most prevalent symptom, namely restlessness. This could be a very useful first step in diagnosing GAD by medical professionals and their less skilled hospital’s IT area using pre diagnostic textual conversations. But it lacks high quality in predicting GAD in most symptoms as found by a low 50% precision on most symptoms (except 5). The AutoML technology is quicker for IT professionals and gives a decent performance, but it can be improved upon by more sophisticated ANN methods like Convolution neural networks that plug AutoML’s symptom’s deficiencies with at least 80+% precision and 0.4+% in F1 score, namely in detecting poorly predicted symptoms of concentration and irritability.
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Karpat, Fatih, Ahmet Emir Dirik, Onur Can Kalay, Oğuz Doğan, and Burak Korcuklu. "Vibration-Based Early Crack Diagnosis With Machine Learning for Spur Gears." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24006.
Повний текст джерелаАнотація:
Abstract Gear mechanisms are one of the most significant components of the power transmission systems. Due to increasing emphasis on the high-speed, longer working life, high torques, etc. cracks may be observed on the gear surface. Recently, Machine Learning (ML) algorithms have started to be used frequently in fault diagnosis with developing technology. The aim of this study is to determine the gear root crack and its degree with vibration-based diagnostics approach using ML algorithms. To perform early crack detection, the single tooth stiffness and the mesh stiffness calculated via ANSYS for both healthy and faulty (25-50-75-100%) teeth. The calculated data transferred to the 6-DOF dynamic model of a one-stage gearbox, and vibration responses was collected. The data gathered for healthy and faulty cases were evaluated for the feature extraction with five statistical indicators. Besides, white Gaussian noise was added to the data obtained from the 6-DOF model, and it was aimed at early fault diagnosis and condition monitoring with ML algorithms. In this study, the gear root crack and its degree analyzed for both healthy and four different crack sizes (25%-50%-75%-100%) for the gear crack detection. Thereby, a method was presented for early fault diagnosis without the need for a big experimental dataset. The proposed vibration-based approach can eliminate the high test rig construction costs and can potentially be used for the evaluation of different working conditions and gear design parameters. Therefore, catastrophic failures can be prevented, and maintenance costs can be optimized by early crack detection.
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Sta. Cruz, Anton Domini, and Michael Angelo Pedrasa. "Exploring PMU Measurement Representation for ML-based Fault Diagnosis Models." In 2019 9th International Conference on Power and Energy Systems (ICPES). IEEE, 2019. http://dx.doi.org/10.1109/icpes47639.2019.9105386.
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Lahlou, Kenza, Sven Inge Oedegaard, Morten Svendsen, Tore Weltzin, Knut Steinar Bjørkevoll, and Bjørn Rudshaug. "Drilling Advisory for Automatic Drilling Control." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204074-ms.
Повний текст джерелаАнотація:
Abstract This paper describes a system being developed for providing an optimized real-time decision support with automatic forward-looking and what-if simulations. It will address the challenge of achieving automation, better performance, and avoidance of non-productive time (NPT) in drilling operations. It will additionally address the demanding human support currently required in the entire decision support workflow. The approach includes utilization of Model based reasoning in Artificial Intelligence (AI) with a Digital Twin combined with Machine Learning (ML) and advanced 3D visualization which is a key enabler for operation alerts and optimization. Multiple forward-looking and what-if simulations will also be run in real-time to find optimal parameters for flow, rotation and running speed. A Diagnostic module will detect abnormalities and trigger safeguards. Auto-configuration and auto-calibration will be the key elements for Drilling Advisory system and deployment without the need for back-office support. The personnel involved in the operation (drilling contractor, service provider and operator) will be able to quickly provide the necessary operational input and then the system will be auto-calibrated during the operation. Results will be an Advisory Tool providing the operation with an optimal flow, rotation speed and running speed during Drilling, Tripping, Casing/liner/screen running and cement operations in two applications areas: In front of the driller as an Advisory tool for rigs with legacy drilling control systems not capable of receiving automated instructions. Base for providing direct commands and safeguards to rigs with control systems capable of receiving automated commands of optimal flow, rotation speed and running speed.
Звіти організацій з теми "ML diagnostic model"
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Ehiabhi, Jolly, and Haifeng Wang. A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0003.
Повний текст джерелаАнотація:
Review question / Objective: A systematic review of Mental health diagnosis/prognoses of mental disorders using Machine Learning techniques with information from biometric signals. A review of the trend and status of these ML techniques in mental health diagnosis and an investigation of how these signals are used to help increase the efficiency of mental health disease diagnosis. Using Machine learning techniques to classify mental health diseases as against using only expert knowledge for diagnosis. Feature Extraction from signal gotten from biometric signals that help classify sleep disorders. Rationale: To review the application of ML techniques on multimodal and multichannel PSG datasets got from biosensors typically used in the Hospital. To help professionals grasp the steps of using machine learning to classify mental health diseases.
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Malkinson, Mertyn, Irit Davidson, Moshe Kotler, and Richard L. Witter. Epidemiology of Avian Leukosis Virus-subtype J Infection in Broiler Breeder Flocks of Poultry and its Eradication from Pedigree Breeding Stock. United States Department of Agriculture, March 2003. http://dx.doi.org/10.32747/2003.7586459.bard.
Повний текст джерелаАнотація:
Objectives 1. Establish diagnostic procedures to identify tolerant carrier birds based on a) Isolation of ALV-J from blood, b) Detection of group-specific antigen in cloacal swabs and egg albumen. Application of these procedures to broiler breeder flocks with the purpose of removing virus positive birds from the breeding program. 2. Survey the AL V-J infection status of foundation lines to estimate the feasibility of the eradication program 3. Investigate virus transmission through the embryonated egg (vertical) and between chicks in the early post-hatch period (horizontal). Establish a model for limiting horizontal spread by analyzing parameters operative in the hatchery and brooder house. 4. Compare the pathogenicity of AL V-J isolates for broiler chickens. 5. Determine whether AL V-J poses a human health hazard by examining its replication in mammalian and human cells. Revisions. The: eradication objective had to be terminated in the second year following the closing down of the Poultry Breeders Union (PBU) in Israel. This meant that their foundation flocks ceased to be available for selection. Instead, the following topics were investigated: a) Comparison of commercial breeding flocks with and without myeloid leukosis (matched controls) for viremia and serum antibody levels. b) Pathogenicity of Israeli isolates for turkey poults. c) Improvement of a diagnostic ELISA kit for measuring ALV-J antibodies Background. ALV-J, a novel subgroup of the avian leukosis virus family, was first isolated in 1988 from broiler breeders presenting myeloid leukosis (ML). The extent of its spread among commercial breeding flocks was not appreciated until the disease appeared in the USA in 1994 when it affected several major breeding companies almost simultaneously. In Israel, ML was diagnosed in 1996 and was traced to grandparent flocks imported in 1994-5, and by 1997-8, ML was present in one third of the commercial breeding flocks It was then realized that ALV-J transmission was following a similar pattern to that of other exogenous ALVs but because of its unusual genetic composition, the virus was able to establish an extended tolerant state in infected birds. Although losses from ML in affected flocks were somewhat higher than normal, both immunosuppression and depressed growth rates were encountered in affected broiler flocks and affected their profitability. Conclusions. As a result of the contraction in the number of international primary broiler breeders and exchange of male and female lines among them, ALV-J contamination of broiler breeder flocks affected the broiler industry worldwide within a short time span. The Israeli national breeding company (PBU) played out this scenario and presented us with an opportunity to apply existing information to contain the virus. This BARD project, based on the Israeli experience and with the aid of the ADOL collaborative effort, has managed to offer solutions for identifying and eliminating infected birds based on exhaustive virological and serological tests. The analysis of factors that determine the efficiency of horizontal transmission of virus in the hatchery resulted in the workable solution of raising young chicks in small groups through the brooder period. These results were made available to primary breeders as a strategy for reducing viral transmission. Based on phylogenetic analysis of selected Israeli ALV-J isolates, these could be divided into two groups that reflected the countries of origin of the grandparent stock. Implications. The availability of a simple and reliable means of screening day old chicks for vertical transmission is highly desirable in countries that rely on imported breeding stock for their broiler industry. The possibility that AL V-J may be transmitted to human consumers of broiler meat was discounted experimentally.