Details

Biomedical Data Mining for Information Retrieval

Methodologies, Techniques, and Applications
Artificial Intelligence and Soft Computing for Industrial Transformation 1. Aufl.

von: Sujata Dash, Subhendu Kumar Pani, S. Balamurugan, Ajith Abraham
207,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	02.08.2021
ISBN/EAN:	9781119711254
Sprache:	englisch
Anzahl Seiten:	448

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Titelbeschreibung

BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

Inhaltsverzeichnis

Preface xv 1 Mortality Prediction of ICU Patients Using Machine Learning Techniques 1 Babita Majhi, Aarti Kashyap and Ritanjali Majhi 1.1 Introduction 2 1.2 Review of Literature 3 1.3 Materials and Methods 8 1.3.1 Dataset 8 1.3.2 Data Pre-Processing 8 1.3.3 Normalization 8 1.3.4 Mortality Prediction 10 1.3.5 Model Description and Development 11 1.4 Result and Discussion 15 1.5 Conclusion 16 1.6 Future Work 16 References 17 2 Artificial Intelligence in Bioinformatics 21 V. Samuel Raj, Anjali Priyadarshini, Manoj Kumar Yadav, Ramendra Pati Pandey, Archana Gupta and Arpana Vibhuti 2.1 Introduction 21 2.2 Recent Trends in the Field of AI in Bioinformatics 22 2.2.1 DNA Sequencing and Gene Prediction Using Deep Learning 24 2.3 Data Management and Information Extraction 26 2.4 Gene Expression Analysis 26 2.4.1 Approaches for Analysis of Gene Expression 27 2.4.2 Applications of Gene Expression Analysis 29 2.5 Role of Computation in Protein Structure Prediction 30 2.6 Application in Protein Folding Prediction 31 2.7 Role of Artificial Intelligence in Computer-Aided Drug Design 38 2.8 Conclusions 42 References 43 3 Predictive Analysis in Healthcare Using Feature Selection 53 Aneri Acharya, Jitali Patel and Jigna Patel 3.1 Introduction 54 3.1.1 Overview and Statistics About the Disease 54 3.1.1.1 Diabetes 54 3.1.1.2 Hepatitis 55 3.1.2 Overview of the Experiment Carried Out 56 3.2 Literature Review 58 3.2.1 Summary 58 3.2.2 Comparison of Papers for Diabetes and Hepatitis Dataset 61 3.3 Dataset Description 70 3.3.1 Diabetes Dataset 70 3.3.2 Hepatitis Dataset 71 3.4 Feature Selection 73 3.4.1 Importance of Feature Selection 74 3.4.2 Difference Between Feature Selection, Feature Extraction and Dimensionality Reduction 74 3.4.3 Why Traditional Feature Selection Techniques Still Holds True? 75 3.4.4 Advantages and Disadvantages of Feature Selection Technique 76 3.4.4.1 Advantages 76 3.4.4.2 Disadvantage 76 3.5 Feature Selection Methods 76 3.5.1 Filter Method 76 3.5.1.1 Basic Filter Methods 77 3.5.1.2 Correlation Filter Methods 77 3.5.1.3 Statistical & Ranking Filter Methods 78 3.5.1.4 Advantages and Disadvantages of Filter Method 80 3.5.2 Wrapper Method 80 3.5.2.1 Advantages and Disadvantages of Wrapper Method 82 3.5.2.2 Difference Between Filter Method and Wrapper Method 82 3.6 Methodology 84 3.6.1 Steps Performed 84 3.6.2 Flowchart 84 3.7 Experimental Results and Analysis 85 3.7.1 Task 1—Application of Four Machine Learning Models 85 3.7.2 Task 2—Applying Ensemble Learning Algorithms 86 3.7.3 Task 3—Applying Feature Selection Techniques 87 3.7.4 Task 4—Appling Data Balancing Technique 94 3.8 Conclusion 96 References 99 4 Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications 103 Deepanshu Bajaj, Bharat Bhushan and Divya Yadav 4.1 Introduction 104 4.2 Basic Architecture and Components of e-Health Architecture 105 4.2.1 Front End Layer 106 4.2.2 Communication Layer 107 4.2.3 Back End Layer 107 4.3 Security Requirements in Healthcare 4.0 108 4.3.1 Mutual-Authentications 109 4.3.2 Anonymity 110 4.3.3 Un-Traceability 111 4.3.4 Perfect—Forward—Secrecy 111 4.3.5 Attack Resistance 111 4.3.5.1 Replay Attack 111 4.3.5.2 Spoofing Attack 112 4.3.5.3 Modification Attack 112 4.3.5.4 MITM Attack 112 4.3.5.5 Impersonation Attack 112 4.4 ICT Pillar’s Associated With HC4.0 113 4.4.1 IoT in Healthcare 4.0 114 4.4.2 Cloud Computing (CC) in Healthcare 4.0 115 4.4.3 Fog Computing (FC) in Healthcare 4.0 116 4.4.4 BigData (BD) in Healthcare 4.0 117 4.4.5 Machine Learning (ML) in Healthcare 4.0 118 4.4.6 Blockchain (BC) in Healthcare 4.0 120 4.5 Healthcare 4.0’s Applications-Scenarios 121 4.5.1 Monitor-Physical and Pathological Related Signals 121 4.5.2 Self-Management, and Wellbeing Monitor, and its Precaution 124 4.5.3 Medication Consumption Monitoring and Smart-Pharmaceutics 124 4.5.4 Personalized (or Customized) Healthcare 125 4.5.5 Cloud-Related Medical Information’s Systems 125 4.5.6 Rehabilitation 126 4.6 Conclusion 126 References 127 5 Improved Social Media Data Mining for Analyzing Medical Trends 131 Minakshi Sharma and Sunil Sharma 5.1 Introduction 132 5.1.1 Data Mining 132 5.1.2 Major Components of Data Mining 132 5.1.3 Social Media Mining 134 5.1.4 Clustering in Data Mining 134 5.2 Literature Survey 136 5.3 Basic Data Mining Clustering Technique 140 5.3.1 Classifier and Their Algorithms in Data Mining 143 5.4 Research Methodology 147 5.5 Results and Discussion 151 5.5.1 Tool Description 151 5.5.2 Implementation Results 152 5.5.3 Comparison Graphs Performance Comparison 156 5.6 Conclusion & Future Scope 157 References 158 6 Bioinformatics: An Important Tool in Oncology 163 Gaganpreet Kaur, Saurabh Gupta, Gagandeep Kaur, Manju Verma and Pawandeep Kaur 6.1 Introduction 164 6.2 Cancer—A Brief Introduction 165 6.2.1 Types of Cancer 166 6.2.2 Development of Cancer 166 6.2.3 Properties of Cancer Cells 166 6.2.4 Causes of Cancer 168 6.3 Bioinformatics—A Brief Introduction 169 6.4 Bioinformatics—A Boon for Cancer Research 170 6.5 Applications of Bioinformatics Approaches in Cancer 174 6.5.1 Biomarkers: A Paramount Tool for Cancer Research 175 6.5.2 Comparative Genomic Hybridization for Cancer Research 177 6.5.3 Next-Generation Sequencing 178 6.5.4 miRNA 179 6.5.5 Microarray Technology 181 6.5.6 Proteomics-Based Bioinformatics Techniques 185 6.5.7 Expressed Sequence Tags (EST) and Serial Analysis of Gene Expression (SAGE) 187 6.6 Bioinformatics: A New Hope for Cancer Therapeutics 188 6.7 Conclusion 191 References 192 7 Biomedical Big Data Analytics Using IoT in Health Informatics 197 Pawan Singh Gangwar and Yasha Hasija 7.1 Introduction 198 7.2 Biomedical Big Data 200 7.2.1 Big EHR Data 201 7.2.2 Medical Imaging Data 201 7.2.3 Clinical Text Mining Data 201 7.2.4 Big OMICs Data 202 7.3 Healthcare Internet of Things (IoT) 202 7.3.1 IoT Architecture 202 7.3.2 IoT Data Source 204 7.3.2.1 IoT Hardware 204 7.3.2.2 IoT Middleware 205 7.3.2.3 IoT Presentation 205 7.3.2.4 IoT Software 205 7.3.2.5 IoT Protocols 206 7.4 Studies Related to Big Data Analytics in Healthcare IoT 206 7.5 Challenges for Medical IoT & Big Data in Healthcare 209 7.6 Conclusion 210 References 210 8 Statistical Image Analysis of Drying Bovine Serum Albumin Droplets in Phosphate Buffered Saline 213 Anusuya Pal, Amalesh Gope and Germano S. Iannacchione 8.1 Introduction 214 8.2 Experimental Methods 216 8.3 Results 217 8.3.1 Temporal Study of the Drying Droplets 217 8.3.2 FOS Characterization of the Drying Evolution 219 8.3.3 GLCM Characterization of the Drying Evolution 220 8.4 Discussions 224 8.4.1 Qualitative Analysis of the Drying Droplets and the Dried Films 224 8.4.2 Quantitative Analysis of the Drying Droplets and the Dried Films 227 8.5 Conclusions 231 Acknowledgments 232 References 232 9 Introduction to Deep Learning in Health Informatics 237 Monika Jyotiyana and Nishtha Kesswani 9.1 Introduction 237 9.1.1 Machine Learning v/s Deep Learning 240 9.1.2 Neural Networks and Deep Learning 241 9.1.3 Deep Learning Architecture 242 9.1.3.1 Deep Neural Networks 243 9.1.3.2 Convolutional Neural Networks 243 9.1.3.3 Deep Belief Networks 244 9.1.3.4 Recurrent Neural Networks 244 9.1.3.5 Deep Auto-Encoder 245 9.1.4 Applications 246 9.2 Deep Learning in Health Informatics 246 9.2.1 Medical Imaging 246 9.2.1.1 CNN v/s Medical Imaging 247 9.2.1.2 Tissue Classification 247 9.2.1.3 Cell Clustering 247 9.2.1.4 Tumor Detection 247 9.2.1.5 Brain Tissue Classification 248 9.2.1.6 Organ Segmentation 248 9.2.1.7 Alzheimer’s and Other NDD Diagnosis 248 9.3 Medical Informatics 249 9.3.1 Data Mining 249 9.3.2 Prediction of Disease 249 9.3.3 Human Behavior Monitoring 250 9.4 Bioinformatics 250 9.4.1 Cancer Diagnosis 250 9.4.2 Gene Variants 251 9.4.3 Gene Classification or Gene Selection 251 9.4.4 Compound–Protein Interaction 251 9.4.5 DNA–RNA Sequences 252 9.4.6 Drug Designing 252 9.5 Pervasive Sensing 252 9.5.1 Human Activity Monitoring 253 9.5.2 Anomaly Detection 253 9.5.3 Biological Parameter Monitoring 253 9.5.4 Hand Gesture Recognition 253 9.5.5 Sign Language Recognition 254 9.5.6 Food Intake 254 9.5.7 Energy Expenditure 254 9.5.8 Obstacle Detection 254 9.6 Public Health 255 9.6.1 Lifestyle Diseases 255 9.6.2 Predicting Demographic Information 256 9.6.3 Air Pollutant Prediction 256 9.6.4 Infectious Disease Epidemics 257 9.7 Deep Learning Limitations and Challenges in Health Informatics 257 References 258 10 Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review 263 Shikha Gupta, Nitish Mehndiratta, Swarnim Sinha, Sangana Chaturvedi and Mehak Singla 10.1 Introduction 263 10.2 Techniques and Algorithms Applied 265 10.3 Analysis of Major Health Disorders Through Different Techniques 267 10.3.1 Alzheimer 267 10.3.2 Dementia 268 10.3.3 Depression 274 10.3.4 Schizophrenia and Bipolar Disorders 281 10.4 Conclusion 285 References 286 11 Deep Learning Applications in Medical Image Analysis 293 Ananya Singha, Rini Smita Thakur and Tushar Patel 11.1 Introduction 294 11.1.1 Medical Imaging 295 11.1.2 Artificial Intelligence and Deep Learning 296 11.1.3 Processing in Medical Images 300 11.2 Deep Learning Models and its Classification 303 11.2.1 Supervised Learning 303 11.2.1.1 RNN (Recurrent Neural Network) 303 11.2.2 Unsupervised Learning 304 11.2.2.1 Stacked Auto Encoder (SAE) 304 11.2.2.2 Deep Belief Network (DBN) 306 11.2.2.3 Deep Boltzmann Machine (DBM) 307 11.2.2.4 Generative Adversarial Network (GAN) 308 11.3 Convolutional Neural Networks (CNN)—A Popular Supervised Deep Model 309 11.3.1 Architecture of CNN 310 11.3.2 Learning of CNNs 313 11.3.3 Medical Image Denoising using CNNs 314 11.3.4 Medical Image Classification Using CNN 316 11.4 Deep Learning Advancements—A Biological Overview 317 11.4.1 Sub-Cellular Level 317 11.4.2 Cellular Level 319 11.4.3 Tissue Level 323 11.4.4 Organ Level 326 11.4.4.1 The Brain and Neural System 326 11.4.4.2 Sensory Organs—The Eye and Ear 329 11.4.4.3 Thoracic Cavity 330 11.4.4.4 Abdomen and Gastrointestinal (GI) Track 331 11.4.4.5 Other Miscellaneous Applications 332 11.5 Conclusion and Discussion 335 References 336 12 Role of Medical Image Analysis in Oncology 351 Gaganpreet Kaur, Hardik Garg, Kumari Heena, Lakhvir Singh, Navroz Kaur, Shubham Kumar and Shadab Alam 12.1 Introduction 352 12.2 Cancer 353 12.2.1 Types of Cancer 354 12.2.2 Causes of Cancer 355 12.2.3 Stages of Cancer 355 12.2.4 Prognosis 356 12.3 Medical Imaging 357 12.3.1 Anatomical Imaging 357 12.3.2 Functional Imaging 358 12.3.3 Molecular Imaging 358 12.4 Diagnostic Approaches for Cancer 358 12.4.1 Conventional Approaches 358 12.4.1.1 Laboratory Diagnostic Techniques 359 12.4.1.2 Tumor Biopsies 359 12.4.1.3 Endoscopic Exams 360 12.4.2 Modern Approaches 361 12.4.2.1 Image Processing 361 12.4.2.2 Implications of Advanced Techniques 362 12.4.2.3 Imaging Techniques 363 12.5 Conclusion 375 References 376 13 A Comparative Analysis of Classifiers Using Particle Swarm Optimization-Based Feature Selection 383 Chandra Sekhar Biswal, Subhendu Kumar Pani and Sujata Dash 13.1 Introduction 384 13.2 Feature Selection for Classification 385 13.2.1 An Overview: Data Mining 385 13.2.2 Classification Prediction 387 13.2.3 Dimensionality Reduction 387 13.2.4 Techniques of Feature Selection 388 13.2.5 Feature Selection: A Survey 392 13.2.6 Summary 394 13.3 Use of WEKA Tool 395 13.3.1 WEKA Tool 395 13.3.2 Classifier Selection 395 13.3.3 Feature Selection Algorithms in WEKA 395 13.3.4 Performance Measure 396 13.3.5 Dataset Description 398 13.3.6 Experiment Design 398 13.3.7 Results Analysis 399 13.3.8 Summary 401 13.4 Conclusion and Future Work 401 13.4.1 Summary of the Work 401 13.4.2 Research Challenges 402 13.4.3 Future Work 404 References 404 Index 409

Autorenportrait

Sujata Dash received her PhD in Computational Modeling from Berhampur University, Orissa, India in 1995. She is an associate professor in P.G. Department of Computer Science & Application, North Orissa University, at Baripada, India. She has published more than 80 technical papers in international journals, conferences, book chapters and has authored 5 books. Subhendu Kumar Pani received his PhD from Utkal University Odisha, India in 2013. He is working as Professor in the Krupajal Computer Academy, BPUT, Odisha, India. S. Balamurugan is the Director-Research and Development, Intelligent Research Consultancy Services(iRCS), Coimbatore, Tamilnadu, India. His PhD is in Infomation Technology and he has published 45 books, 200+ international journals/conferences and 35 patents. Ajith Abraham received PhD in Computer Science from Monash University, Melbourne, Australia in 2001. He is Director of Machine Intelligence Research Labs (MIR Labs) which has members from 100+ countries. Ajith’s research experience includes over 30 years in the industry and academia. He has authored / co-authored over 1300+ publications (with colleagues from nearly 40 countries) and has an h-index of 86+.

Back cover copy

This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.