{"product_id":"applied-smart-health-care-informatics-isbn-9781119743170","title":"Applied Smart Health Care Informatics","description":"Applied Smart Health Care Informatics \u003cp\u003e\u003cb\u003eExplores how intelligent systems offer new opportunities for optimizing the acquisition, storage, retrieval, and use of information in healthcare\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eApplied Smart Health Care Informatics\u003c\/i\u003e explores how health information technology and intelligent systems can be integrated and deployed to enhance healthcare management. Edited and authored by leading experts in the field, this timely volume introduces modern approaches for managing existing data in the healthcare sector by utilizing artificial intelligence (AI), meta-heuristic algorithms, deep learning, the Internet of Things (IoT), and other smart technologies.  \u003c\/p\u003e\u003cp\u003eDetailed chapters review advances in areas including machine learning, computer vision, and soft computing techniques, and discuss various applications of healthcare management systems such as medical imaging, electronic medical records (EMR), and drug development assistance. Throughout the text, the authors propose new research directions and highlight the smart technologies that are central to establishing proactive health management, supporting enhanced coordination of care, and improving the overall quality of healthcare services. \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eProvides an overview of different deep learning applications for intelligent healthcare informatics management \u003c\/li\u003e \u003cli\u003eDescribes novel methodologies and emerging trends in artificial intelligence and computational intelligence and their relevance to health information engineering and management\u003c\/li\u003e \u003cli\u003eProposes IoT solutions that disseminate essential medical information for intelligent healthcare management\u003c\/li\u003e \u003cli\u003eDiscusses mobile-based healthcare management, content-based image retrieval, and computer-aided diagnosis using machine and deep learning techniques\u003c\/li\u003e \u003cli\u003eExamines the use of exploratory data analysis in intelligent healthcare informatics systems \u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Smart Health Care Informatics: A Computational Intelligence Perspective\u003c\/i\u003e is an invaluable text for graduate students, postdoctoral researchers, academic lecturers, and industry professionals working in the area of healthcare and intelligent soft computing. \u003c\/p\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAbout the Editors xix\u003c\/p\u003e \u003cp\u003eList of Contributors xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 An Overview of Applied Smart Health Care Informatics in the Context of Computational Intelligence 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSourav De and Rik Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Big Data Analytics in Healthcare 2\u003c\/p\u003e \u003cp\u003e1.3 AI in Healthcare 3\u003c\/p\u003e \u003cp\u003e1.4 Cloud Computing in Healthcare 4\u003c\/p\u003e \u003cp\u003e1.5 IoT in Healthcare 4\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 5\u003c\/p\u003e \u003cp\u003eReferences 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 A Review on Deep Learning Method for Lung Cancer Stage Classification Using PET-CT 9\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKaushik Pratim Das, Chandra J, and Dr Nachamai M\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 9\u003c\/p\u003e \u003cp\u003e2.1.1 Scope of the Research 10\u003c\/p\u003e \u003cp\u003e2.1.2 TNM Staging 11\u003c\/p\u003e \u003cp\u003e2.1.2.1 TNM Descriptors for Staging per IASLC Guidelines 11\u003c\/p\u003e \u003cp\u003e2.1.2.2 PET-CT Scan in Lung Cancer Imaging 12\u003c\/p\u003e \u003cp\u003e2.2 Related Works 12\u003c\/p\u003e \u003cp\u003e2.2.1 Artificial Intelligence in Medical Imaging 14\u003c\/p\u003e \u003cp\u003e2.2.2 Classification for Medical Imaging 14\u003c\/p\u003e \u003cp\u003e2.2.2.1 Deep Learning 15\u003c\/p\u003e \u003cp\u003e2.2.2.2 Image Classification Using Deep-learning Techniques 15\u003c\/p\u003e \u003cp\u003e2.3 Methods 15\u003c\/p\u003e \u003cp\u003e2.3.1 Transfer Learning 15\u003c\/p\u003e \u003cp\u003e2.3.2 AlexNet 16\u003c\/p\u003e \u003cp\u003e2.3.3 AlexNet Architecture 16\u003c\/p\u003e \u003cp\u003e2.3.4 Experimental Setup 17\u003c\/p\u003e \u003cp\u003e2.3.4.1 Image Processing 18\u003c\/p\u003e \u003cp\u003e2.3.4.2 Data Augmentation 19\u003c\/p\u003e \u003cp\u003e2.3.4.3 Training and Validation 19\u003c\/p\u003e \u003cp\u003e2.4 Results and Discussion 19\u003c\/p\u003e \u003cp\u003e2.4.1 Primary Tumor (T) 19\u003c\/p\u003e \u003cp\u003e2.4.2 Metastasis (M) 21\u003c\/p\u003e \u003cp\u003e2.4.3 Lymph Node (N) 21\u003c\/p\u003e \u003cp\u003e2.4.4 Classification Accuracy of AlexNet 24\u003c\/p\u003e \u003cp\u003e2.4.5 Comparative Analysis 25\u003c\/p\u003e \u003cp\u003e2.4.6 Limitations 26\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 26\u003c\/p\u003e \u003cp\u003eReferences 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Formal Methods for the Security of Medical Devices 31\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSrinivas Pinisetty, Nathan Allen, Hammond Pearce, Mark Trew, Manoj Singh Gaur, and Partha Roop\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 31\u003c\/p\u003e \u003cp\u003e3.1.1 Pacemaker Security 33\u003c\/p\u003e \u003cp\u003e3.1.2 Overview 34\u003c\/p\u003e \u003cp\u003e3.2 Background: Cardiac Pacemakers 34\u003c\/p\u003e \u003cp\u003e3.2.1 Pacemakers 35\u003c\/p\u003e \u003cp\u003e3.2.1.1 Operation of a DDD Mode Pacemaker 36\u003c\/p\u003e \u003cp\u003e3.2.2 The Cardiac System 37\u003c\/p\u003e \u003cp\u003e3.2.2.1 Electrograms and Electrocardiograms 38\u003c\/p\u003e \u003cp\u003e3.3 State of the Art, Formal Verification Techniques 39\u003c\/p\u003e \u003cp\u003e3.3.1 Formal Verification Techniques 40\u003c\/p\u003e \u003cp\u003e3.3.1.1 Static Verification Techniques 41\u003c\/p\u003e \u003cp\u003e3.3.1.2 Dynamic Verification Techniques 42\u003c\/p\u003e \u003cp\u003e3.3.2 Runtime Verification 43\u003c\/p\u003e \u003cp\u003e3.3.2.1 A Brief Overview of Some Runtime Verification Frameworks 44\u003c\/p\u003e \u003cp\u003e3.3.3 Correcting Execution of a System at Runtime (Runtime Enforcement) 45\u003c\/p\u003e \u003cp\u003e3.3.3.1 Runtime Enforcement of Untimed Properties 46\u003c\/p\u003e \u003cp\u003e3.3.3.2 Runtime Enforcement Approaches for Timed Properties 46\u003c\/p\u003e \u003cp\u003e3.4 Formal Runtime-Based Approaches for Medical Device Security 47\u003c\/p\u003e \u003cp\u003e3.4.1 Overview of the Approach 47\u003c\/p\u003e \u003cp\u003e3.4.2 Mapping EGM Properties to ECG Properties 48\u003c\/p\u003e \u003cp\u003e3.4.3 Security of Pacemakers Using Runtime Verification 49\u003c\/p\u003e \u003cp\u003e3.4.3.1 Timed Words, Timed Languages, and Defining Timed Properties 50\u003c\/p\u003e \u003cp\u003e3.4.3.2 Runtime Verification Monitor 51\u003c\/p\u003e \u003cp\u003e3.4.3.3 Architecture of the Monitoring System 53\u003c\/p\u003e \u003cp\u003e3.4.3.4 Implementation of the ECG Processing and RV Monitor Modules 53\u003c\/p\u003e \u003cp\u003e3.4.3.5 Summary of Experiments and Results 54\u003c\/p\u003e \u003cp\u003e3.4.4 Securing Pacemakers with Runtime Enforcement Hardware 54\u003c\/p\u003e \u003cp\u003e3.4.4.1 Preliminaries: Words, Languages, and Defining Properties as DTA 55\u003c\/p\u003e \u003cp\u003e3.4.4.2 Runtime Enforcement Monitor 56\u003c\/p\u003e \u003cp\u003e3.4.4.3 Verification of the Enforcer Hardware 58\u003c\/p\u003e \u003cp\u003e3.4.4.4 How Does the Enforcer Prevent Security Attacks? 58\u003c\/p\u003e \u003cp\u003e3.4.4.5 Summary of Experiments and Results 59\u003c\/p\u003e \u003cp\u003e3.5 Summary 59\u003c\/p\u003e \u003cp\u003eReferences 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Integrating Two Deep Learning Models to Identify Gene Signatures in Head and Neck Cancer from  Multi-Omics Data 67\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSuparna Saha, Sumanta Ray, and Sanghamitra Bandyopadhyay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 67\u003c\/p\u003e \u003cp\u003e4.2 Related Work 68\u003c\/p\u003e \u003cp\u003e4.3 Materials and Methods 70\u003c\/p\u003e \u003cp\u003e4.3.1 A Brief Introduction of the Capsule Network 70\u003c\/p\u003e \u003cp\u003e4.3.2 An Introduction to Autoencoders 71\u003c\/p\u003e \u003cp\u003e4.4 Results 72\u003c\/p\u003e \u003cp\u003e4.4.1 Data Set Details 72\u003c\/p\u003e \u003cp\u003e4.4.1.1 Gene Expression Data (Illumina Hiseq) 72\u003c\/p\u003e \u003cp\u003e4.4.1.2 Human Methylation 450K 73\u003c\/p\u003e \u003cp\u003e4.4.2 Architecture of Autoencoder Model 73\u003c\/p\u003e \u003cp\u003e4.4.3 Architecture of the Proposed Capsule Network Model 74\u003c\/p\u003e \u003cp\u003e4.4.4 Validation of Two Deep Learning Models 75\u003c\/p\u003e \u003cp\u003e4.4.5 Gene Signatures from Primary Capsules 76\u003c\/p\u003e \u003cp\u003e4.5 Discussion 77\u003c\/p\u003e \u003cp\u003eAcknowledgments 78\u003c\/p\u003e \u003cp\u003eReferences 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 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 83\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSoham Choudhuri, Saurav Mallik, Bhaswar Ghosh, Tapas Si, Tapas Bhadra, Ujjwal Maulik, and Aimin Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 83\u003c\/p\u003e \u003cp\u003e5.2 Biological Terms 84\u003c\/p\u003e \u003cp\u003e5.3 Single-Cell Sequencing (scRNA-seq) Data 86\u003c\/p\u003e \u003cp\u003e5.3.1 Computational Methods for Interpreting scRNA-seq Data 86\u003c\/p\u003e \u003cp\u003e5.3.1.1 Visualizing and Clustering Cells 86\u003c\/p\u003e \u003cp\u003e5.3.1.2 Inference and Branching Analysis of Cellular Trajectory 86\u003c\/p\u003e \u003cp\u003e5.3.1.3 Identifying Highly Variable Genes 86\u003c\/p\u003e \u003cp\u003e5.3.1.4 Identifying Marker and Differentially Expressed Genes 90\u003c\/p\u003e \u003cp\u003e5.4 Methods of Multi-Omic Data Integration 90\u003c\/p\u003e \u003cp\u003e5.4.1 Unsupervised Data Integration Methods 91\u003c\/p\u003e \u003cp\u003e5.4.1.1 Matrix Factorization Methods 91\u003c\/p\u003e \u003cp\u003e5.4.1.2 Bayesian Methods 91\u003c\/p\u003e \u003cp\u003e5.4.1.3 Network-Based Methods 94\u003c\/p\u003e \u003cp\u003e5.4.1.4 Multi-Step Analysis and Multiple Kernel Learning 94\u003c\/p\u003e \u003cp\u003e5.4.2 Supervised Data Integration 95\u003c\/p\u003e \u003cp\u003e5.4.2.1 Network-Based Methods 95\u003c\/p\u003e \u003cp\u003e5.4.2.2 Multiple Kernel Learning 95\u003c\/p\u003e \u003cp\u003e5.4.2.3 Multi-Step Analysis 95\u003c\/p\u003e \u003cp\u003e5.4.3 Semi-Supervised Data Integration 95\u003c\/p\u003e \u003cp\u003e5.4.3.1 GeneticInterPred 97\u003c\/p\u003e \u003cp\u003e5.5 AI Drug Discovery 97\u003c\/p\u003e \u003cp\u003e5.5.1 AI Primary Drug Screening 97\u003c\/p\u003e \u003cp\u003e5.5.1.1 Cell Sorting and Classification with Image Analysis 97\u003c\/p\u003e \u003cp\u003e5.5.2 AI Secondary Drug Screening 99\u003c\/p\u003e \u003cp\u003e5.5.2.1 Physical Properties Predictions 99\u003c\/p\u003e \u003cp\u003e5.5.2.2 Predictions of Bio-Activity 99\u003c\/p\u003e \u003cp\u003e5.5.2.3 Prediction of Toxicity 99\u003c\/p\u003e \u003cp\u003e5.5.3 AI in Drug Design 99\u003c\/p\u003e \u003cp\u003e5.5.3.1 Prediction of Target Protein 3D Structures 99\u003c\/p\u003e \u003cp\u003e5.5.3.2 Predicting Drug-Protein Interactions 100\u003c\/p\u003e \u003cp\u003e5.5.4 Planning Chemical Synthesis with AI 100\u003c\/p\u003e \u003cp\u003e5.5.4.1 Retro-Synthesis Pathway Prediction 100\u003c\/p\u003e \u003cp\u003e5.5.4.2 Reaction Yield Predictions and Reaction Mechanism Insights 100\u003c\/p\u003e \u003cp\u003e5.6 Medical Imaging Data Analysis 100\u003c\/p\u003e \u003cp\u003e5.6.1 Analysis: Radio-Mic Quantification 101\u003c\/p\u003e \u003cp\u003e5.6.2 Analysis: Bio-Marker Identification 101\u003c\/p\u003e \u003cp\u003e5.7 Applying IoT (Internet of Things) to Biomedical Research 102\u003c\/p\u003e \u003cp\u003e5.7.1 IoT and IoMT Applications for Healthcare and Well-Being 102\u003c\/p\u003e \u003cp\u003e5.7.1.1 Wireless Medical Devices 102\u003c\/p\u003e \u003cp\u003e5.8 Conclusions 102\u003c\/p\u003e \u003cp\u003eAcknowledgments 102\u003c\/p\u003e \u003cp\u003eReferences 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Association Rule Mining Based on Ethnic Groups and Classification using Super Learning 111\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMd Faisal Kabir and Simone A. Ludwig\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 111\u003c\/p\u003e \u003cp\u003e6.2 Background 112\u003c\/p\u003e \u003cp\u003e6.3 Motivation and Contribution 114\u003c\/p\u003e \u003cp\u003e6.4 Data Analysis 115\u003c\/p\u003e \u003cp\u003e6.4.1 Data Description 115\u003c\/p\u003e \u003cp\u003e6.4.2 Data Preprocessing 115\u003c\/p\u003e \u003cp\u003e6.4.3 Further Preprocessing for Ethnic Group Rule Discovery with Multiple Consequences 115\u003c\/p\u003e \u003cp\u003e6.4.3.1 Transaction-Like Database for Association Rule 115\u003c\/p\u003e \u003cp\u003e6.4.4 Classification Data Set 116\u003c\/p\u003e \u003cp\u003e6.5 Methodology 117\u003c\/p\u003e \u003cp\u003e6.5.1 Association Rule Mining 117\u003c\/p\u003e \u003cp\u003e6.5.2 Super Learning 118\u003c\/p\u003e \u003cp\u003e6.5.2.1 Ensemble or Super Learner Set-Up 118\u003c\/p\u003e \u003cp\u003e6.6 Experiments and Results 119\u003c\/p\u003e \u003cp\u003e6.6.1 Rules Discovery 120\u003c\/p\u003e \u003cp\u003e6.6.1.1 Rules of Breast Cancer Patients Based on Ethnic Groups 120\u003c\/p\u003e \u003cp\u003e6.6.1.2 Interpreting Rules 120\u003c\/p\u003e \u003cp\u003e6.6.2 Evaluation Criteria of Classification Model 121\u003c\/p\u003e \u003cp\u003e6.6.2.1 Super Learner Results 124\u003c\/p\u003e \u003cp\u003e6.6.3 Discussion 125\u003c\/p\u003e \u003cp\u003e6.7 Conclusion and Future Work 126\u003c\/p\u003e \u003cp\u003eReferences 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Neuro-Rough Hybridization for Recognition of Virus Particles from TEM Images 131\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDebamita Kumar and Pradipta Maji\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 131\u003c\/p\u003e \u003cp\u003e7.2 Existing Approaches for Virus Particle Classification 132\u003c\/p\u003e \u003cp\u003e7.3 Proposed Algorithm 134\u003c\/p\u003e \u003cp\u003e7.3.1 Extraction of Local Textural Features 135\u003c\/p\u003e \u003cp\u003e7.3.2 Selection of Class-Pair Relevant Features 135\u003c\/p\u003e \u003cp\u003e7.3.3 Extraction of Discriminating Features 138\u003c\/p\u003e \u003cp\u003e7.3.4 Classification 139\u003c\/p\u003e \u003cp\u003e7.4 Experimental Results and Discussion 140\u003c\/p\u003e \u003cp\u003e7.4.1 Experimental Setup 140\u003c\/p\u003e \u003cp\u003e7.4.2 Methods Compared 140\u003c\/p\u003e \u003cp\u003e7.4.3 Database Considered 141\u003c\/p\u003e \u003cp\u003e7.4.4 Effectiveness of Proposed Approach 141\u003c\/p\u003e \u003cp\u003e7.4.5 Comparative Performance Analysis 143\u003c\/p\u003e \u003cp\u003e7.4.5.1 Comparison with Deep Architectures 144\u003c\/p\u003e \u003cp\u003e7.4.5.2 Comparison with Existing Approaches 145\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 146\u003c\/p\u003e \u003cp\u003eReferences 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Neural Network Optimizers for Brain Tumor Image Detection 151\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eT. Kalaiselvi and S.T. Padmapriya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 151\u003c\/p\u003e \u003cp\u003e8.2 Related Works 152\u003c\/p\u003e \u003cp\u003e8.3 Background 153\u003c\/p\u003e \u003cp\u003e8.3.1 Types of Neural Networks 153\u003c\/p\u003e \u003cp\u003e8.3.2 Tunable Elements of Neural Networks 154\u003c\/p\u003e \u003cp\u003e8.3.2.1 Basic Parameters 154\u003c\/p\u003e \u003cp\u003e8.3.2.2 Hyperparameters 154\u003c\/p\u003e \u003cp\u003e 8.3.2.3 Regularization Techniques 155\u003c\/p\u003e \u003cp\u003e8.3.2.4 Neural Network Optimizers 156\u003c\/p\u003e \u003cp\u003e8.4 Case Study - Brain Tumor Detection 157\u003c\/p\u003e \u003cp\u003e8.4.1 Methodology 157\u003c\/p\u003e \u003cp\u003e8.4.2 Data Sets and Metrics 157\u003c\/p\u003e \u003cp\u003e8.4.3 Results and Discussion 159\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 162\u003c\/p\u003e \u003cp\u003eReferences 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Abnormal Slice Classification from MRI Volumes using the Bilateral Symmetry of Human Head Scans 165\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eN. Kalaichelvi, T. Kalaiselvi, and K. Somasundaram\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 165\u003c\/p\u003e \u003cp\u003e9.1.1 MRIs of the Human Brain 165\u003c\/p\u003e \u003cp\u003e9.1.2 Normal and Abnormal Slices 166\u003c\/p\u003e \u003cp\u003e9.1.3 Background 167\u003c\/p\u003e \u003cp\u003e9.1.3.1 Decision Tree Classifiers 167\u003c\/p\u003e \u003cp\u003e9.1.3.2 K-Nearest Neighbours (KNN) Classifiers 168\u003c\/p\u003e \u003cp\u003e9.1.3.3 Support Vector Machine (SVM) 168\u003c\/p\u003e \u003cp\u003e9.1.3.4 Naive Bayes 169\u003c\/p\u003e \u003cp\u003e9.1.3.5 Artificial Neural Network (ANN) 169\u003c\/p\u003e \u003cp\u003e9.1.3.6 Back-Propagation Neural Network (BPN) 170\u003c\/p\u003e \u003cp\u003e9.1.3.7 Random Forest Classifiers 170\u003c\/p\u003e \u003cp\u003e9.2 Literature Review 171\u003c\/p\u003e \u003cp\u003e9.3 Methodology 172\u003c\/p\u003e \u003cp\u003e9.3.1 Preprocessing 173\u003c\/p\u003e \u003cp\u003e9.3.2 Feature Extraction 174\u003c\/p\u003e \u003cp\u003e9.3.3 Feature Selection 175\u003c\/p\u003e \u003cp\u003e9.3.4 Classification 177\u003c\/p\u003e \u003cp\u003e9.3.5 Cross-Validation 177\u003c\/p\u003e \u003cp\u003e9.3.6 Training Validation and Testing 178\u003c\/p\u003e \u003cp\u003e9.4 Materials and Metrics 179\u003c\/p\u003e \u003cp\u003e9.4.1 Confusion Matrix 179\u003c\/p\u003e \u003cp\u003e9.5 Results and Discussion 180\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 182\u003c\/p\u003e \u003cp\u003eReferences 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Conclusion 187\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSiddhartha Bhattacharyya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eReferences 188\u003c\/p\u003e \u003cp\u003eIndex 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDr. Sourav De,\u003c\/b\u003e Associate Professor, Department of Computer Science and Engineering, Cooch Behar Government Engineering College, India. \u003c\/p\u003e \u003cp\u003e\u003cb\u003eDr. Rik Das,\u003c\/b\u003e Assistant Professor, Department of Information Technology, Xavier Institute of Social Service, India. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDr. Siddhartha Bhattacharyya,\u003c\/b\u003e Principal, Rajnagar Mahavidyalaya, India.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDr. Ujjwal Maulik, \u003c\/b\u003eProfessor, Department of Computer Science and Engineering, Jadavpur University, India.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eExplores how intelligent systems offer new opportunities for optimizing the acquisition, storage, retrieval, and use of information in healthcare\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eApplied Smart Health Care Informatics\u003c\/i\u003e explores how health information technology and intelligent systems can be integrated and deployed to enhance healthcare management. Edited and authored by leading experts in the field, this timely volume introduces modern approaches for managing existing data in the healthcare sector by utilizing artificial intelligence (AI), meta-heuristic algorithms, deep learning, the Internet of Things (IoT), and other smart technologies.  \u003c\/p\u003e\u003cp\u003eDetailed chapters review advances in areas including machine learning, computer vision, and soft computing techniques, and discuss various applications of healthcare management systems such as medical imaging, electronic medical records (EMR), and drug development assistance. Throughout the text, the authors propose new research directions and highlight the smart technologies that are central to establishing proactive health management, supporting enhanced coordination of care, and improving the overall quality of healthcare services. \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eProvides an overview of different deep learning applications for intelligent healthcare informatics management \u003c\/li\u003e \u003cli\u003eDescribes novel methodologies and emerging trends in artificial intelligence and computational intelligence and their relevance to health information engineering and management\u003c\/li\u003e \u003cli\u003eProposes IoT solutions that disseminate essential medical information for intelligent healthcare management\u003c\/li\u003e \u003cli\u003eDiscusses mobile-based healthcare management, content-based image retrieval, and computer-aided diagnosis using machine and deep learning techniques\u003c\/li\u003e \u003cli\u003eExamines the use of exploratory data analysis in intelligent healthcare informatics systems \u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Smart Health Care Informatics: A Computational Intelligence Perspective\u003c\/i\u003e is an invaluable text for graduate students, postdoctoral researchers, academic lecturers, and industry professionals working in the area of healthcare and intelligent soft computing.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988753826021,"sku":"NP9781119743170","price":134.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119743170.jpg?v=1761781458","url":"https:\/\/k12savings.com\/es\/products\/applied-smart-health-care-informatics-isbn-9781119743170","provider":"K12savings","version":"1.0","type":"link"}