Preface xxi Part I: Introduction to Healthcare Systems 1 1 Role of Technology in Healthcare Systems 3A. Hency Juliet and K. Kalaiselvi 1.1 Introduction 4 1.2 Transformation in Healthcare 7 1.3 Technology Transformation in Healthcare Industry 14 1.4 Patient Care Improvement Using Healthcare Technology 16 1.5 Importance of Technology in Healthcare 18 1.6 Technology Impact on Healthcare 19 1.7 Innovation and Digital Transformation 21 1.8 Diagnostics' Role in Combatting Life-Threatening Diseases 23 1.9 Role of Medical Technology in Healthcare 25 1.10 Conclusion 27 2 Health Status Estimation based on Daily Life Activities 31Josephine Anitha A. and Geetanjali R. 2.1 Introduction 32 2.2 Intersection of Technology and Healthcare 34 2.3 Unveiling the Technologies 38 2.4 Machine Learning Marvels: Unravelling Health Insights From Daily Life Activities 39 2.5 Data Collection and Processing in Daily Life Health Monitoring 41 2.6 Ethical Considerations, Data Privacy, and Regulatory Compliance 44 2.7 Potential Areas of Improvement 46 2.8 Challenges and Opportunities 47 2.9 Conclusion 49 3 Decision Support System in Healthcare Monitoring 55V. Suganthi and K. Kalaiselvi 3.1 Introduction 56 3.2 Components of a Healthcare Monitoring System 65 3.3 Role of Decision Support System 70 3.4 Challenges in Implementing Decision Support Systems 72 3.5 Future Trends and Innovations 74 3.6 Conclusion 75 4 Vision-Based Management System in Healthcare Applications 79K. Balasubramanian, Anu Tonk, Seema Bhakuni, S. Anita, Freddy Ajila and S. Sathish Kumar 4.1 Introduction 80 4.2 History 86 4.3 Tear Testing and Ocular Surface Analysis in a Clinical Examination 86 4.4 Other Ocular Surface Health-Related Clinical Examinations 89 4.5 Management of ADDE 95 4.6 Disease-Specific Therapy in ADDE 99 4.7 ADDE With NK 101 4.8 Unmet Needs and Future Directions 101 4.9 Conclusion 102 5 Semantic Framework in Healthcare Systems 107Pooja Dabhowale, Mukesh Yadav, Nidhi Tiwari, Ruchi Sharma, Jose Anand A. and Irshad Ahamad 5.1 Introduction 108 5.2 Background 109 5.3 Internet of Things 111 5.4 Research Methodology 115 5.5 Theoretical Framework 117 5.6 Data Analysis 120 5.7 Conclusion 125 Part II: AI-Based System Models in Healthcare Applications 131 6 Predictive Analysis in Healthcare Systems 133J. Sathya and F. Mary Harin Fernandez 6.1 Introduction 134 6.2 Related Work 136 6.3 Proposed System 142 6.4 Provide Support Tools and Visualizations to Aid in the Decision-Making Process 148 6.5 Conclusion 149 7 Machine Learning in Healthcare System 153A. Hency Juliet and K. Sathya 7.1 Introduction 154 8 Deep Learning Applications in Healthcare Systems 179V. Sheeja Kumari and Renjith Balu 8.1 Introduction 180 8.2 Fundamentals of Deep Learning 182 8.3 Deep Learning Architecture for Image Classification 194 8.4 Conclusion 200 9 Image Analysis for Health Prediction 205Pulla Sujarani and K. Kalaiselvi 9.1 Introduction 206 9.2 Overview 208 9.3 Image Preprocessing 209 9.4 Image Filtering 211 9.5 Image Enhancement 213 9.6 Image Segmentation 215 9.7 Feature Extraction 219 9.8 Classification 222 9.9 Conclusion 226 10 Machine Learning in Biomedical Text Processing 229Shibi Mathai and K. Kalaiselvi 10.1 Introduction 230 10.2 Fundamentals of ML for Text Processing 232 10.3 NLP Techniques in Biomedicine 233 10.4 NLP Techniques in Biomedicine 236 10.5 Feature Engineering and Selection in Biomedical Text 238 10.6 Applications of ML in Biomedical Text Mining 240 10.7 Evaluation Metrics and Model Validation 243 10.8 Ethical Considerations and Data Privacy 245 10.9 Future Directions and Challenges 246 10.10 Conclusion 247 11 Decision Making Biomedical Support System 253V. Sheeja Kumari, J. Vijila and Renjith Balu 11.1 Introduction 254 11.2 System Architecture and Components 258 11.3 Machine Learning Algorithms 268 11.4 Expert Systems 269 11.5 Statistical Analysis Tools 270 11.6 User Interface 272 11.7 Interactivity for Healthcare Professionals 274 11.8 User-Friendly Design 275 11.9 Summary 277 Part III: Modernization and Future -- Healthcare Applications 281 12 Medical Imaging and Diagnostics with Machine Learning 283M. Sowmiya, D. Bhanu, K. Shruthi, Punitha Jilt, B. Beaula Pinky and A. Yasmine Begum 12.1 Introduction 284 12.2 Establishing a Smart Sensor Network With the Help of AI 285 12.3 Impact of Nanotechnology and IoMT in Healthcare 292 12.4 Artificial Intelligence’s Impact on the Surgery 297 12.5 The Importance of Artificial Intelligence in Treating Diabetes and Cancer 300 12.6 Challenges and Future Scope 304 12.7 Conclusions 305 13 Predicting Ventilation Needs in Intensive Care Unit 311Yashini Priyankha S., S. Sumathi, T. Mangavarkarasi, Jose Anand A. and Mithileysh Sathiyanarayanan 13.1 Introduction 312 13.2 AI-Based Predictive Models for Healthcare Ventilation Systems 313 13.3 AI Based Ventilator Weaning Predicting Unit 319 13.4 Predictive Applications of AI in Healthcare 321 13.5 AI Impacts on Ventilation Requirements 323 13.6 ICU and Healthcare Future With AI 324 13.7 Conclusion 325 14 Modernized Health Record Maintenance 329K. Balasubadra, Franklin Baltodano, Indira Pineda, S. Mayakannan, Eduardo Hernández and Navin M. George 14.1 Introduction 330 14.2 Literature Survey 335 14.3 Materials and Methods 336 14.4 Having a Proper Strategy 345 14.5 A Common Database to be Maintained Like a Repository 345 14.6 The Database Must Have Genuine Data 345 14.7 Case Study and Applications 345 14.8 Conclusion 357 15 Natural Language Processing in Medical Applications 361V. Prasanna Srinivasan, Evelyn Rosero, P. Sengottuvelan, Abhinav Singhal, Chandraketu Singh and S. Mayakannan 15.1 Introduction 362 15.2 Related Studies on Medical Systems - Use of Machine Learning 363 15.3 Health Data Formats in Medical Systems 365 15.4 Prototype of Algorithms and Data Conversion 367 15.5 Results and Discussion 371 15.6 Conclusions 384 16 Chat Bots for Medical Enquiries 389K. Saravanan, Indira Pineda, Franklin Baltodano, Krunal Vishavadia, Vanessa Valverde and Jose Anand A. 16.1 Introduction 390 16.2 Artificial Intelligence - Chatbot: Components of Architecture 398 16.3 Artificial Intelligence - Chatbot: Models for Generating a Response 400 16.4 AI Chatbots: Methods and Technologies 402 16.5 A Development of Conversational Agents: State-of-the-Art Chatbots 406 16.6 AI Chatbots: Customer-Based Services 414 16.7 AI-Chatbots: Public Administration-Based Services 417 16.8 Chatbot Performance Evaluation 419 16.9 Conclusion 421 17 Secured Health Insurance Management 425A. Ravisankar, P. Manikandan, Iskandar Muda, Shrinivas V. Kulkarni, Rolando Marcel Torres Castillo and Jose Anand A. 17.1 Introduction 426 17.2 Methods 431 17.3 Results 436 17.4 Discussion 440 17.5 Conclusion 445 18 Future of Healthcare Applications 449Vettrivel Arul, Hitendra Kumar Lautre, T. Priya, Satish Kumar Verma, Freddy Ajila and Ramu Samineni 18.1 Introduction 450 18.2 A History of Blockchain Technology (1991 - 2021) 454 18.3 Motivations 457 18.4 Topmost Healthcare Projects in Blockchain Technology Based on Market Capital 459 18.5 Healthcare Applications for Blockchain Technology 463 18.6 Research Challenges and Future Direction 476 18.7 Conclusion 479 References 480 Index 483

Artificial Intelligence-Based System Models in Healthcare provides a comprehensive and insightful guide to the transformative applications of AI in the healthcare system. This book is a groundbreaking exploration of the synergies between artificial intelligence and healthcare innovation. In an era where technological advancements are reshaping the landscape of medical practices, this book provides a comprehensive and insightful guide to the transformative applications of AI in healthcare systems. From conceptual foundations to practical implementations, the book serves as a roadmap for understanding the intricate relationships between AI-based system models and the evolution of healthcare delivery. The first section delves into the fundamental role of technology in reshaping the healthcare landscape. With a focus on daily life activities, decision support systems, vision-based management, and semantic frameworks, this section lays the groundwork for understanding the pivotal role of AI in revolutionizing traditional healthcare approaches. Each chapter offers a unique perspective, emphasizing the intricate integration of technology into healthcare ecosystems. The second section takes a deep dive into specific applications of AI, ranging from predictive analysis and machine learning to deep learning, image analysis, and biomedical text processing. With a focus on decision-making support systems, this section aims to demystify the complex world of AI algorithms in healthcare, offering valuable insights into their practical implications and potential impact on patient outcomes. The final section addresses the modernization of healthcare practices and envisions the future landscape of AI applications. From medical imaging and diagnostics to predicting ventilation needs in intensive care units, modernizing health record maintenance, natural language processing, chatbots for medical inquiries, secured health insurance management, and glimpses into the future, the book concludes by exploring the frontiers of AI-driven healthcare innovations. Audience This book is intended for researchers and postgraduate students in artificial intelligence and the biomedical and healthcare sectors. Medical administrators, policymakers and regulatory specialists will also have an interest.