Explainable Artificial Intelligence in Medical Decision Support Systems

Inbunden, Engelska, 2023

Av Agbotiname Lucky Imoize, Jude Hemanth, Dinh-Thuan Do, Samarendra Nath Sur, Nigeria) Imoize, Agbotiname Lucky (Lecturer, University of Lagos, Department of Electrical and Electronics Engineering, India) Hemanth, Jude (Professor, Karunya University, Coimbatore, USA) Do, Dinh-Thuan (Research Scientist, University of Colorado Denver, Department of Electrical Engineering, India) Sur, Samarendra Nath (Assistant Professor, Sikkim Manipal Institute of Technology, Department of Electronics & Communication Engineering

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Medical decision support systems (MDSS) are computer-based programs that analyse data within a patient's healthcare records to provide questions, prompts, or reminders to assist clinicians at the point of care. Inputting a patient's data, symptoms, or current treatment regimens into an MDSS, clinicians are assisted with the identification or elimination of the most likely potential medical causes, which can enable faster discovery of a set of appropriate diagnoses or treatment plans. Explainable AI (XAI) is a "white box" model of artificial intelligence in which the results of the solution can be understood by the users, who can see an estimate of the weighted importance of each feature on the model's predictions, and understand how the different features interact to arrive at a specific decision.This book discusses XAI-based analytics for patient-specific MDSS as well as related security and privacy issues associated with processing patient data. It provides insights into real-world scenarios of the deployment, application, management, and associated benefits of XAI in MDSS. The book outlines the frameworks for MDSS and explores the applicability, prospects, and legal implications of XAI for MDSS. Applications of XAI in MDSS such as XAI for robot-assisted surgeries, medical image segmentation, cancer diagnostics, and diabetes mellitus and heart disease prediction are explored.

Produktinformation

Utgivningsdatum2023-01-30
Mått156 x 234 x 30 mm
Vikt1 066 g
FormatInbunden
SpråkEngelska
SerieHealthcare Technologies
Antal sidor545
FörlagInstitution of Engineering and Technology
ISBN9781839536205

Tillhör följande kategorier

Hälsoinformatik inom Medicin
Biovetenskap inom Naturvetenskap och teknik

Agbotiname Lucky Imoize is a lecturer in the Department of Electrical and Electronics Engineering at the University of Lagos, Nigeria and a research scholar at Ruhr University Bochum, Germany. He is a Fulbright fellow, a senior member of the IEEE, and a registered engineer. He has published over 90 articles and conference papers, two edited textbooks, and five book chapters. His research interests are 6G wireless communication, artificial intelligence, and the Internet of Things.Jude Hemanth is a professor at Karunya University, Coimbatore, India. He has delivered more than 100 keynote talks and invited lectures at international conferences and workshops, and has authored more than 100 research papers and 27 edited books. His research areas include computational intelligence and image processing.Dinh-Thuan Do is a research scientist in the Department of Electrical Engineering, University of Colorado Denver, USA. He won a Golden Globe Award from the Vietnam Ministry of Science and Technology in 2015. He has published over 200 journal and conference papers, one textbook, two edited books and eight book chapters. His research interests include signal processing in wireless communications networks, full-duplex transmission, and energy harvesting.Samarendra Nath Sur is an assistant professor in the Department of Electronics & Communication Engineering at Sikkim Manipal Institute of Technology, India. His research interests include wireless communication, signal processing, and remote sensing. He has published more than 77 SCI/Scopus indexed international journal and conference papers, four edited textbooks, and five book chapters. He is a senior member of the IEEE and associate member of the Institution of Engineers (India) (IEI).

Chapter 1: Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): healthcare systems perspectiveChapter 2: Explainable artificial intelligence (XAI) in medical decision support systems (MDSS): applicability, prospects, legal implications, and challengesChapter 3: Explainable Artificial Intelligence-based framework for medical decision support systemsChapter 4: Prototype interface for detecting mental fatigue with EEG and XAI frameworks in Industry 4.0Chapter 5: XAI for medical image segmentation in medical decision support systemsChapter 6: XAI robot-assisted surgeries in future medical decision support systemsChapter 7: Prediction of erythemato squamous-disease using ensemble learning frameworkChapter 8: Security-based explainable artificial intelligence (XAI) in healthcare systemChapter 9: Explainable dimensionality reduction model with deep learning for diagnosing hypertensive retinopathyChapter 10: Understanding cancer patients with diagnostically influential factors using high-dimensional data embeddingChapter 11: Explainable neural networks in diabetes mellitus predictionChapter 12: A KNN and ANN model for predicting heart diseasesChapter 13: Artificial Intelligence-enabled Internet of Medical Things for COVID-19 pandemic data managementChapter 14: A deep neural network for the identification of lead molecules in antibiotics discoveryChapter 15: Statistical test with differential privacy for medical decision support systemsChapter 16: Automated decision support system for diagnosing sleep diseases using machine intelligence techniquesChapter 17: XAI methods for precision medicine in medical decision support systemsChapter 18: The psychology of explanation in medical decision support systems