Design and Forecasting Models for Disease Management

1. Auflage 2025
ISBN: 978-1-394-23404-2
Verlag: Wiley

Buch, Englisch, 336 Seiten

The book provides an essential overview of AI techniques in disease management and how these computational methods can lead to further innovations in healthcare.

Design and Forecasting Models for Disease Management is a resourceful volume of 13 chapters that elaborates on computational methods and how AI techniques can aid in smart disease management. It contains several statistical and AI techniques that can be used to acquire data on many different diseases. The main objective of this book is to demonstrate how AI techniques work for early disease detection and forecasting useful information for medical experts. As such, this volume intends to serve as a resource to elicit and elaborate on possible intelligent mechanisms for helping detect early signs of diseases. Additionally, the book examines numerous machine learning and data analysis techniques in the biomedical field that are used for detecting and forecasting disease management at the cellular level. It discusses various applications of image segmentation, data analysis techniques, and hybrid machine learning techniques for illnesses, and encompasses modeling, prediction, and diagnosis of disease data.

Audience

Researchers, engineers and graduate students in the fields of computational biology, information technology, bioinformatics, and epidemiology.

Autoren/Hrsg.

Dutta, Pijush

Mandal, Sudip

Cengiz, Korhan

Sadhu, Arindam

Jana, Gour Gopal

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Biomedizin, Medizinische Forschung, Klinische Studien
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik

Inhaltsverzeichnis

Preface xvii

Part 1: Safety and Regulatory Aspects for Disease Pre-Screening 1

1 A Study of Possible AI Aversion in Healthcare Consumers 3
Tanupriya Mukherjee and Anusriya Mukherjee

1.1 Introduction to AI in Healthcare 4

1.1.1 The Role of AI in Transforming Healthcare 5

1.1.2 The Unfolding Paradigm: Potential Benefits and Challenges of AI Implementation in Healthcare 6

1.1.3 Overview of Consumer Receptivity Towards AI in Medicine: A Comparative Analysis 7

1.2 Consumer Reluctance to Utilize AI in Healthcare: Present Scenario 8

1.2.1 Top Factors Influencing Consumer Resistance to Medical AI 10

1.2.2 Uncovering the Psychological Barriers and Concerns Associated with AI Adoption in Healthcare 11

1.2.3 Case Studies and Research Findings on Consumer Aversion to AI-Based Healthcare Services 13

1.2.4 Impact on Consumer Decision-Making 14

1.2.5 Effects of AI Aversion on Consumer Decision-Making Processes: An Analysis 15

1.2.6 Understanding How Consumer Perceptions Influence Their Choice Between Human and AI Healthcare Providers 15

1.2.7 Exploring Role of Trust, Perceived Competence and Empathy in Consumer Preferences 16

1.3 Economic Implications of AI Aversion 17

1.3.1 Investigating Influence of AI Aversion on Consumer Willingness to Pay for Healthcare Services 19

1.3.2 Influence of Patient Education on AI Aversion in Healthcare 19

1.3.3 Influence of Patient Awareness on AI Aversion in Healthcare 21

1.3.4 Influence of Age of Patient on AI Aversion in Healthcare 21

1.4 Overcoming Resistance to Medical AI 22

1.4.1 Strategies for Enhancing Consumer Trust and Acceptance of AI in Healthcare 23

1.4.2 Approaches to Alleviate Consumer Concerns and Misconceptions: Communication and Education 24

1.4.3 Cases of Successful Implementation of AI Technologies in Healthcare and Lessons Learned 25

1.5 Ethical Considerations and Governance 26

1.5.1 Regulatory Frameworks for Ethical AI Operations to Fight Aversion in Healthcare Consumers 27

1.5.2 Addressing the Potential Cost-Effectiveness and Affordability Concerns Associated with AI-Based Healthcare Solutions 28

1.5.3 Balancing Privacy, Data Protection and Need for Transparency in AI Healthcare Applications 29

1.6 Future Outlook and Opportunities 31

1.6.1 The Future of AI in Healthcare and Its Impact on Consumer Aversion 32

1.6.2 Exploring Emerging Technologies and Trends That May Alleviate Consumer Concerns 33

1.6.3 Opportunities for Collaboration Between AI Developers, Healthcare Providers, and Consumers 34

1.6.4 Summary of Key Findings on Consumer Aversion to AI in Healthcare 35

1.6.5 Implications for Healthcare Practitioners, Policymakers and Researchers 36

1.7 Conclusion 37

References 38

2 A Study of AI Application Through Integrated and Systematic Moral Cognitive Therapy in the Healthcare Sector 47
Anusriya Mukherjee, Tanupriya Mukherjee and Mili Mitra Roy

2.1 Introduction 48

2.1.1 Understanding the Role of AI in Healthcare 49

2.1.2 Advantages of AI in Healthcare 50

2.1.3 Moral Dilemmas and AI-Based Healthcare 52

2.2 What is Integrated and Systematic Moral Cognitive Therapy (ISMCT)? 54

2.2.1 Integrating Moral Cognitive Therapy with AI 55

2.2.2 Alignment of Moral Cognitive Therapy Principles with AI Applications 56

2.2.3 Benefits of Integrated and Systematic Moral Cognitive Therapy 57

2.2.4 Applications of AI-Integrated Moral Cognitive Therapy in Healthcare 58

2.3 The Role of AI in Healthcare: A Fine Balance Between Ethics and Innovation 61

2.3.1 Humanizing Healthcare: Towards an AI-ISMCT 62

2.3.2 Synergized AI and ISMCT 63

2.3.3 Case Study and Success Stories 64

2.4 Advancing Research in AI-Integrated Moral Cognitive Therapy 67

2.4.1 Collaborative Efforts Between Healthcare Professionals and AI Developers 68

2.4.2 Implications for Policy and Regulatory Frameworks 69

2.5 Conclusion 70

References 70

3 A Strategic Model to Control Non-Communicable Diseases 77
Soumik Gangopadhyay, Amitava Ukil, Soma Sur and Saugat Ghosh

3.1 Introduction 78

3.1.1 India and NCDs 78

3.2 Survey of Literature 84

3.2.1 Factors Contributing to the Growth of NCDs 84

3.2.2 Lifestyle Modification – A Strategic Role in Mitigation of NCD 85

3.2.3 Policy to Control NCDs 86

3.3 Proposed Model 87

3.3.1 Registration and Information Centre (RIC) 88

3.3.2 Integration Centre (IIC) 88

3.3.3 Strategic Review Centre (SRC) 89

3.3.4 Expected Outcome of the Proposed Model 90

3.4 Conclusion 91

References 92

4 Image Compression Technique Using Color Filter Array (CFA) for Disease Diagnosis and Treatment 99
Indrani Dalui, Avisek Chatterjee, Surajit Goon and Pubali Das Sarkar

4.1 Introduction 100

4.1.1 Color Filter Array 100

4.1.2 Electronic Health Record (EHR) 101

4.2 Related Works 102

4.3 Proposed Model 108

4.4 Implementation 110

4.5 Results 111

4.6 Conclusion 112

References 113

5 Research in Image Processing for Medical Applications Using the Secure Smart Healthcare Technique 115
Debraj Modak and Chowdhury Jaminur Rahaman

5.1 Introduction 116

5.1.1 Imaging Systems 118

5.1.2 The Digital Image Processing System 119

5.1.3 Image Enhancement 120

5.2 Classification of Digital Images 121

5.2.1 Utilizations of Digital Image Processing (DIP) 121

5.2.1.1 Medicine 121

5.2.1.2 Forensics 122

5.2.2 Medical Image Analysis 122

5.2.3 Max-Variance Automatic Cut-Off Method 122

5.2.4 Medical Imaging Segmentation 124

5.2.5 Image-Based on Edge Detection 124

5.2.5.1 Robert’s Kernel Method 125

5.2.5.2 Prewitt Kernel 125

5.2.5.3 Sobel

Über Autor(innen)

Pijush Dutta, PhD, is an assistant professor and head of the Department of Electronics and Communication Engineering at Greater Kolkata College of Engineering and Management, West Bengal, India, with over 11 years of teaching and over seven years of research experience. He has published eight books, as well as 14 patents and over 100 research articles in national and international journals and conferences. His research interests include sensors and transducers, nonlinear process control systems, the Internet of Things (IoT), and machine and deep learning.

Sudip Mandal, PhD, is an assistant professor in the Electronics and Communication Engineering Department at Jalpaiguri Government Engineering College, India. He has over 50 publications in national and international peer-reviewed journals and conferences, as well as two Indian patents and two books. He is a member of the Institute of Electrical and Electronics Engineers’ Computational Intelligence Society.

Korhan Cengiz, PhD, is an associate professor in the Department of Computer Engineering at Istinye University, Istanbul, Turkey. He has published over 40 articles in international peer-reviewed journals, five international patents, and edited over ten books. His research interests include wireless sensor networks, wireless communications, and statistical signal processing.

Arindam Sadhu, PhD, is an assistant professor in the Electronics and Communication Engineering Department at Swami Vivekananda University, West Bengal, India, with over five years of teaching and over three years of research experience. He has published two international patents and over ten articles in national and international journals and conferences. His research interests include post-complementary metal-oxide-semiconductor transistors, quantum computing, and quantum dot cellular automata.

Gour Gopal Jana is an assistant professor in the Electronics and Communication Engineering Department at Greater Kolkata College of Engineering and Management, West Bengal, India, with over 13 years of teaching and over three years of research experience. He has published two international patents and over ten research articles in national and international journals and conference proceedings. His research interests include metal thin film sensors, biosensors, nanobiosensors, and nanocomposites.