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Applied Smart Health Care Informatics


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      Table of Contents

      1  Cover

      2  Title Page

      3  Copyright

      4  Dedication

      5  Preface

      6  About the Editors

      7  List of Contributors

      8  1 An Overview of Applied Smart Health Care Informatics in the Context of Computational Intelligence 1.1 Introduction 1.2 Big Data Analytics in Healthcare 1.3 AI in Healthcare 1.4 Cloud Computing in Healthcare 1.5 IoT in Healthcare 1.6 Conclusion References Note

      9  2 A Review on Deep Learning Method for Lung Cancer Stage Classification Using PET‐CT 2.1 Introduction 2.2 Related Works 2.3 Methods 2.4 Results and Discussion 2.5 Conclusion References Note

      10  3 Formal Methods for the Security of Medical Devices1 3.1 Introduction 3.2 Background: Cardiac Pacemakers 3.3 State of the Art, Formal Verification Techniques 3.4 Formal Runtime‐Based Approaches for Medical Device Security 3.5 Summary References Notes

      11  4 Integrating Two Deep Learning Models to Identify Gene Signatures in Head and Neck Cancer from Multi‐Omics Data 4.1 Introduction 4.2 Related Work 4.3 Materials and Methods 4.4 Results 4.5 Discussion Acknowledgments References Note

      12  5 A Review of Computational Learning and IoT Applications to High‐Throughput Array‐Based Sequencing and Medical Imaging Data in Drug Discovery and Other Health Care Systems 5.1 Introduction 5.2 Biological Terms 5.3 Single‐Cell Sequencing (scRNA‐seq) Data 5.4 Methods of Multi‐Omic Data Integration 5.5 AI Drug Discovery 5.6 Medical Imaging Data Analysis 5.7 Applying IoT (Internet of Things) to Biomedical Research 5.8 Conclusions Acknowledgments References Note

      13  6 Association Rule Mining Based on Ethnic Groups and Classification using Super Learning1 6.1 Introduction 6.2 Background 6.3 Motivation and Contribution 6.4 Data Analysis 6.5 Methodology 6.6 Experiments and Results 6.7 Conclusion and Future Work References Notes

      14  7 Neuro‐Rough Hybridization for Recognition of Virus Particles from TEM Images1 7.1 Introduction 7.2 Existing Approaches for Virus Particle Classification 7.3 Proposed Algorithm 7.4 Experimental Results and Discussion 7.5 Conclusion References Notes