Explainable Artificial Intelligence in Medical Decision Support Systems
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 MDS...
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| Main Authors | , , , |
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| Format | eBook |
| Language | English |
| Published |
Stevenage
The Institution of Engineering and Technology
2022
Institution of Engineering and Technology (The IET) Institution of Engineering & Technology Institution of Engineering and Technology |
| Edition | 1 |
| Series | Healthcare technologies series |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9781839536205 1839536209 |
| DOI | 10.1049/PBHE050E |
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| Abstract | 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. |
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| AbstractList | This edited book gives insights into the deployment, application, management, and benefits of explainable artificial intelligence (XAI) in medical decision support systems (MDSS). The book discusses XAI-based analytics for patient-specific MDSS as well as related security and privacy issues. 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. 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. 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. |
| Author | Hemanth Jude Imoize Agbotiname Lucky Sur Samarendra Nath Do Dinh-Thuan |
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| Copyright | 2022 |
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| DOI | 10.1049/PBHE050E |
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| Editor | Do, Dinh-Thuan Sur, Samarendra Nath Hemanth, Jude Imoize, Agbotiname Lucky |
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| Publisher | The Institution of Engineering and Technology Institution of Engineering and Technology (The IET) Institution of Engineering & Technology Institution of Engineering and Technology |
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| Snippet | Medical decision support systems (MDSS) are computer-based programs that analyse data within a patient's healthcare records to provide questions, prompts, or... This edited book gives insights into the deployment, application, management, and benefits of explainable artificial intelligence (XAI) in medical decision... |
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| SubjectTerms | Artificial intelligence Biochemistry, Biology & Biotechnology Biomedical Biotechnology General References Life Sciences Life sciences: general issues Medical bioinformatics Medical informatics SCIENCE Software Engineering TECHNOLOGY & ENGINEERING |
| TableOfContents | Chapter 1: Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): healthcare systems perspective -- Chapter 2: Explainable artificial intelligence (XAI) in medical decision support systems (MDSS): applicability, prospects, legal implications, and challenges -- Chapter 3: Explainable Artificial Intelligence-based framework for medical decision support systems -- Chapter 4: Prototype interface for detecting mental fatigue with EEG and XAI frameworks in Industry 4.0 -- Chapter 5: XAI for medical image segmentation in medical decision support systems -- Chapter 6: XAI robot-assisted surgeries in future medical decision support systems -- Chapter 7: Prediction of erythemato squamous-disease using ensemble learning framework -- Chapter 8: Security-based explainable artificial intelligence (XAI) in healthcare system -- Chapter 9: Explainable dimensionality reduction model with deep learning for diagnosing hypertensive retinopathy -- Chapter 10: Understanding cancer patients with diagnostically influential factors using high-dimensional data embedding -- Chapter 11: Explainable neural networks in diabetes mellitus prediction -- Chapter 12: A KNN and ANN model for predicting heart diseases -- Chapter 13: Artificial Intelligence-enabled Internet of Medical Things for COVID-19 pandemic data management -- Chapter 14: A deep neural network for the identification of lead molecules in antibiotics discovery -- Chapter 15: Statistical test with differential privacy for medical decision support systems -- Chapter 16: Automated decision support system for diagnosing sleep diseases using machine intelligence techniques -- Chapter 17: XAI methods for precision medicine in medical decision support systems -- Chapter 18: The psychology of explanation in medical decision support systems Title Page Preface Table of Contents 1. Explainable Artificial Intelligence (XAI) in Medical Decision Systems (MDSSs): Healthcare Systems Perspective 2. Explainable Artificial Intelligence (XAI) in Medical Decision Support Systems (MDSS): Applicability, Prospects, Legal Implications, and Challenges 3. Explainable Artificial Intelligence-Based Framework for Medical Decision Support Systems 4. Prototype Interface for Detecting Mental Fatigue with EEG and XAI Frameworks in Industry 4.0 5. XAI for Medical Image Segmentation in Medical Decision Support Systems 6. XAI Robot-Assisted Surgeries in Future Medical Decision Support Systems 7. Prediction of Erythemato Squamous-Disease Using Ensemble Learning Framework 8. Security-Based Explainable Artificial Intelligence (XAI) in Healthcare System 9. Explainable Dimensionality Reduction Model with Deep Learning for Diagnosing Hypertensive Retinopathy 10. Understanding Cancer Patients with Diagnostically Influential Factors Using High-Dimensional Data Embedding 11. Explainable Neural Networks in Diabetes Mellitus Prediction 12. A KNN and ANN Model for Predicting Heart Diseases 13. Artificial Intelligence-Enabled Internet of Medical Things for COVID-19 Pandemic Data Management 14. A Deep Neural Network for the Identification of Lead Molecules in Antibiotics Discovery 15. Statistical Test with Differential Privacy for Medical Decision Support Systems 16. Automated Decision Support System for Diagnosing Sleep Diseases Using Machine Intelligence Techniques 17. XAI Methods for Precision Medicine in Medical Decision Support Systems 18. The Psychology of Explanation in Medical Decision Support Systems Index Intro -- Contents -- About the Editors -- Preface -- Acknowledgments -- 1. Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): healthcare systems perspective | Oluwafisayo Babatope Ayoade, Tinuke Omolewa Oladele, Agbotiname Lucky Imoize, Joseph Bamidele Awotunde, Adetoye Jerome Adeloye, Segun Omotayo Olorunyomi and Ayorinde Oladele Idowu -- 1.1 Introduction -- 1.2 Overview of HMDSSs -- 1.3 Case study of XAI enabled with MDSSs in various infectious diseases -- 1.4 XAI research trends and open issues -- Acknowledgment -- References -- 2. Explainable artificial intelligence (XAI) in medical decision support systems (MDSS): applicability, prospects, legal implications, and challenges | Joseph Bamidele Awotunde, Emmanuel Abidemi Adeniyi, Sunday Adeola Ajagbe, Agbotiname Lucky Imoize, Olukayode Ayodele Oki and Sanjay Misra -- 2.1 Introduction -- 2.2 MDSS overview in healthcare systems -- 2.3 AI in MDSS -- 2.4 XAI -- 2.5 Ethical effects and implications -- 2.6 Conclusion and future directions -- Acknowledgment -- References -- 3. Explainable Artificial Intelligence-based framework for medical decision support systems | Joseph Bamidele Awotunde, Oluwafisayo Babatope Ayoade, Panigrahi Ranjit, Amik Garg and Akash Kumar Bhoi -- 3.1 Introduction -- 3.2 Applicability of XAI in MDSSs -- 3.3 The challenges in the applicability of XAI in MDSSs -- 3.4 The proposed DeepSHAP enabled with DNN framework -- 3.5 Experimental design for cancer prediction -- 3.6 Experimental results -- 3.7 The future research direction of XAI in healthcare systems -- 3.8 Conclusion and future scopes -- References -- 4. Prototype interface for detecting mental fatigue with EEG and XAI frameworks in Industry 4.0 | Martın Montes Rivera, Luciano Martinez, Alberto Ochoa Zezzatti, Alan Navarro, Jesus Rodarte and Nestor Lopez -- 4.1 Introduction -- 4.2 Related work 10. Understanding cancer patients with diagnostically influential factors using high-dimensional data embedding | Ameer Sohail Syed, Hajderanj Laureta, Kun Guo and Daqing Chen -- 10.1 Introduction -- 10.2 Literature review -- 10.3 Dimensionality reduction methods -- 10.4 Methodology -- 10.5 Experiments -- 10.6 Discussion of results -- 10.7 Concluding remarks and future work -- References -- Appendix -- 11. Explainable neural networks in diabetes mellitus prediction | Solomon Chiekezi Nwaneri, Chika Yinka-Banjo, Ugochi Chinomso Uregbulam, Oluwakemi Ololade Odukoya and Agbotiname Lucky Imoize -- 11.1 Introduction -- 11.2 Related work -- 11.3 Methodology -- 11.4 Results and discussion -- 11.5 Conclusion and future scope -- Acknowledgment -- References -- 12. A KNN and ANN model for predicting heart diseases | Sulaiman Olaniyi Abdulsalam, Micheal Olaolu Arowolo, Enobong Chidera Udofot, Ayodeji Matthew Sanni, Damilola David Popoola and Marion Olubunmi Adebiyi -- 12.1 Introduction -- 12.2 Overview of the literature -- 12.3 Materials and methods -- 12.4 Results and discussions -- 12.5 Conclusions -- References -- 13. Artificial Intelligence-enabled Internet of Medical Things for COVID-19 pandemic data management | Agbotiname Lucky Imoize, Peter Anuoluwapo Gbadega, Hope Ikoghene Obakhena, Daisy Osarugue Irabor, K.V.N. Kavitha and Chinmay Chakraborty -- 13.1 Introduction -- 13.2 Related work -- 13.3 IoMT for COVID-19 pandemic data management -- 13.4 Reducing the workload of the medical industry -- 13.5 Privacy-aware energy-efficient framework using AIoMT for COVID-19 -- 13.6 Open research issues -- 13.7 Conclusion -- Acknowledgment -- References -- 14. A deep neural network for the identification of lead molecules in antibiotics discovery | Michael Idowu Oladunjoye, Olumide Olayinka Obe and Olufunso Dayo Alowolodu -- 14.1 Introduction 14.2 Literature review -- 14.3 Materials and methods -- 14.4 Results and discussion -- 14.5 Conclusion -- References -- 15. Statistical test with differential privacy for medical decision support systems | Yuichi Sei, Akihiko Ohsuga and Agbotiname Lucky Imoize -- 15.1 Introduction -- 15.2 Related work -- 15.3 Proposed algorithm -- 15.4 Evaluation -- 15.5 Discussion -- 15.6 Conclusion -- Acknowledgment -- References -- 14 A deep neural network for the identification of lead molecules in antibiotics discovery | Michael Idowu Oladunjoye, Olumide Olayinka Obe and Olufunso Dayo Alowolodu -- 14.1 Introduction -- 14.2 Literature review -- 14.3 Materials and methods -- 14.4 Results and discussion -- 14.5 Conclusion -- References -- 15. Statistical test with differential privacy for medical decision support systems | Yuichi Sei, Akihiko Ohsuga and Agbotiname Lucky Imoize -- 15.1 Introduction -- 15.2 Related work -- 15.3 Proposed algorithm -- 15.4 Evaluation -- 15.5 Discussion -- 15.6 Conclusion -- Acknowledgment -- References -- 16. Automated decision support system for diagnosing sleep diseases using machine intelligence techniques | Santosh Kumar Satapathy, Bidita Khandelwal, Amik Garg and Akash Kumar Bhoi -- 16.1 Introduction -- 16.2 Related work -- 16.3 Experimental dataset -- 16.4 Proposed automatic sleep stage detection method -- 16.5 Classification -- 16.6 Experimental discussion -- 16.7 Conclusion -- References -- 17. XAI methods for precision medicine in medical decision support systems | Abasiama Godwin Akpan, Flavious Bobuin Nkubli, Jeremiah Chinonso Mbazor, Geofery Luntsi and Offiong Udeme -- 17.1 Introduction -- 17.2 Related works -- 17.3 Explainable models in MDSS: opportunities and challenges -- 17.4 Conclusion -- References 18. The psychology of explanation in medical decision support systems | Vitalis Afebuame Iguoba and Agbotiname Lucky Imoize -- 18.1 Introduction -- 18.2 Recent development of XAI in MDSS -- 18.3 Potential benefits of XAI in MDSS -- 18.4 Key challenges of XAI in MDSS -- 18.5 The future of XAI in MDSS -- 18.6 The research trend of XAI in MDSS -- 18.7 The future directions and recommendations -- 18.8 Conclusions and future scope -- Acknowledgment -- References -- Index 4.3 Materials and methods -- 4.4 Results and discussions -- 4.5 Conclusions -- References -- 5. XAI for medical image segmentation in medical decision support systems | Abasiama Godwin Akpan, Flavious Bobuin Nkubli, Victoria Nnaemeka Ezeano, Anayo Christian Okwor, Mabel Chikodili Ugwuja and Udeme Offiong -- 5.1 Introduction -- 5.2 Related work -- 5.3 Analysis of the proposed system -- 5.4 Conclusion -- References -- 6. XAI robot-assisted surgeries in future medical decision support systems | Aishat Titilola Rufai, Kenechi Franklin Dukor, Opeyemi Michael Ageh and Agbotiname Lucky Imoize -- 6.1 Introduction -- 6.2 Related work -- 6.3 Medical robots -- 6.4 Explanation methods -- 6.5 Conclusion -- Acknowledgment -- References -- 7. Prediction of erythemato squamous-disease using ensemble learning framework | Efosa Charles Igodan, Olumide Olayinka Obe, Aderonke Favour-Bethy Thompson and Otasowie Owolafe -- 7.1 Introduction -- 7.2 Related literature review -- 7.3 Materials and methods -- 7.4 Experimental results and discussion -- 7.5 Conclusion -- References -- 8. Security-based explainable artificial intelligence (XAI) in healthcare system | Huseyin Guruler, Naveed Islam and Alloud Din -- 8.1 Introduction -- 8.2 Literature review -- 8.3 Methodology -- 8.4 Experimental result -- 8.5 Conclusion and future scope -- Acknowledgment -- References -- 9. Explainable dimensionality reduction model with deep learning for diagnosing hypertensive retinopathy | Micheal Olaolu Arowolo, Hadassah Oluwadamilola Olumuyiwa, Ruth Omorinsola Adesina, Royal Afonime, Mobayonle Ayodeji Ajayi and Paul Adeoye Omosebi -- 9.1 Introduction -- 9.2 Overview and related works -- 9.3 Materials and methods -- 9.4 Results and discussions -- 9.5 Conclusions -- References |
| Title | Explainable Artificial Intelligence in Medical Decision Support Systems |
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