Search

Browse Subject Areas

For Authors

Submit a Proposal

Biomedical Data Mining for Information Retrieval

Methodologies, Techniques and Applications

Edited by Sujata Dash, Subhendu Kumar Pani, S. Balamurugan and Ajith Abraham
Series: Artificial Intelligence and Soft Computing for Industrial Transformation
Copyright: 2021   |   Status: Published
ISBN: 9781119711247  |  Hardcover  |  
436 pages | 74 illustrations
Price: $225 USD
Add To Cart

One Line Description
This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with
broad coverage of basic scientific applications.

Audience
Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

Description
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.

Back to Top
Author / Editor Details
Sujata Dash, PhD 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, and book chapters and has authored 5 books.

Subhendu Kumar Pani, PhD received his PhD from Utkal University Odisha, India in 2013. He is working as a professor in the Krupajal Computer Academy, BPUT, Odisha, India.

S. Balamurugan, PhD is the Director of Research and Development, Intelligent Research Consultancy Services(iRCS), Coimbatore, Tamilnadu, India. His PhD is in Information Technology and he has published 45 books, 200+ international journals/conferences, and 35 patents.

Ajith Abraham, PhD received his 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 to Top

Table of Contents
Preface
1. Mortality Prediction of ICU Patients Using Machine Learning Techniques Babita Majhi, Aarti Kashyap and Ritanjali Majhi
1.1 Introduction
1.2 Review of Literature
1.3 Materials and Methods
1.3.1 Dataset
1.3.2 Data Pre-Processing
1.3.3 Normalization
1.3.4 Mortality Prediction
1.3.5 Model Description and Development
1.4 Result and Discussion
1.5 Conclusion
1.6 Future Work
References
2. Artificial Intelligence in Bioinformatics
V. Samuel Raj, Anjali Priyadarshini, Manoj Kumar Yadav, Ramendra Pati Pandey,
Archana Gupta and Arpana Vibhuti
2.1 Introduction
2.2 Recent Trends in the Field of AI in Bioinformatics
2.2.1 DNA Sequencing and Gene Prediction Using Deep Learning
2.3 Data Management and Information Extraction
2.4 Gene Expression Analysis
2.4.1 Approaches for Analysis of Gene Expression
2.4.2 Applications of Gene Expression Analysis
2.5 Role of Computation in Protein Structure Prediction
2.6 Application in Protein Folding Prediction
2.7 Role of Artificial Intelligence in Computer-Aided Drug Design
2.8 Conclusions
References
3. Predictive Analysis in Healthcare Using Feature Selection
Aneri Acharya, Jitali Patel and Jigna Patel
3.1 Introduction
3.1.1 Overview and Statistics About the Disease
3.1.1.1 Diabetes
3.1.1.2 Hepatitis
3.1.2 Overview of the Experiment Carried Out
3.2 Literature Review
3.2.1 Summary
3.2.2 Comparison of Papers for Diabetes and Hepatitis Dataset
3.3 Dataset Description
3.3.1 Diabetes Dataset
3.3.2 Hepatitis Dataset
3.4 Feature Selection
3.4.1 Importance of Feature Selection
3.4.2 Difference Between Feature Selection, Feature Extraction and Dimensionality Reduction
3.4.3 Why Traditional Feature Selection Techniques Still Holds True?
3.4.4 Advantages and Disadvantages of Feature Selection Technique
3.4.4.1 Advantages
3.4.4.2 Disadvantage
3.5 Feature Selection Methods
3.5.1 Filter Method
3.5.1.1 Basic Filter Methods
3.5.1.2 Correlation Filter Methods
3.5.1.3 Statistical & Ranking Filter Methods
3.5.1.4 Advantages and Disadvantages of Filter Method
3.5.2 Wrapper Method
3.5.2.1 Advantages and Disadvantages of Wrapper Method
3.5.2.2 Difference Between Filter Method and Wrapper Method
3.6 Methodology
3.6.1 Steps Performed
3.6.2 Flowchart
3.7 Experimental Results and Analysis
3.7.1 Task 1—Application of Four Machine Learning Models
3.7.2 Task 2—Applying Ensemble Learning Algorithms
3.7.3 Task 3—Applying Feature Selection Techniques
3.7.4 Task 4—Appling Data Balancing Technique
3.8 Conclusion
References
4 Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications
Deepanshu Bajaj, Bharat Bhushan and Divya Yadav
4.1 Introduction
4.2 Basic Architecture and Components of e-Health Architecture
4.2.1 Front End Layer
4.2.2 Communication Layer
4.2.3 Back End Layer
4.3 Security Requirements in Healthcare 4.0
4.3.1 Mutual-Authentications
4.3.2 Anonymity
4.3.3 Un-Traceability
4.3.4 Perfect—Forward—Secrecy
4.3.5 Attack Resistance
4.3.5.1 Replay Attack
4.3.5.2 Spoofing Attack
4.3.5.3 Modification Attack
4.3.5.4 MITM Attack
4.3.5.5 Impersonation Attack
4.4 ICT Pillar’s Associated With HC 4.0
4.4.1 IoT in Healthcare 4.0
4.4.2 Cloud Computing (CC) in Healthcare 4.0
4.4.3 Fog Computing (FC) in Healthcare 4.0
4.4.4 BigData (BD) in Healthcare 4.0
4.4.5 Machine Learning (ML) in Healthcare 4.0
4.4.6 Blockchain (BC) in Healthcare 4.0
4.5 Healthcare 4.0’s Applications-Scenarios
4.5.1 Monitor-Physical and Pathological Related Signals
4.5.2 Self-Management, and Wellbeing Monitor, and its Precaution
4.5.3 Medication Consumption Monitoring and Smart-Pharmaceutics
4.5.4 Personalized (or Customized) Healthcare
4.5.5 Cloud-Related Medical Information’s Systems
4.5.6 Rehabilitation
4.6 Conclusion
References
5. Improved Social Media Data Mining for Analyzing Medical Trends Minakshi Sharma and Sunil Sharma
5.1 Introduction
5.1.1 Data Mining
5.1.2 Major Components of Data Mining
5.1.3 Social Media Mining
5.1.4 Clustering in Data Mining
5.2 Literature Survey
5.3 Basic Data Mining Clustering Technique
5.3.1 Classifier and Their Algorithms in Data Mining
5.4 Research Methodology
5.5 Results and Discussion
5.5.1 Tool Description
5.5.2 Implementation Results
5.5.3 Comparison Graphs Performance Comparison
5.6 Conclusion & Future Scope
References
6. Bioinformatics: An Important Tool in Oncology
Gaganpreet Kaur, Saurabh Gupta, Gagandeep Kaur, Manju Verma and Pawandeep Kaur
6.1 Introduction
6.2 Cancer—A Brief Introduction
6.2.1 Types of Cancer
6.2.2 Development of Cancer
6.2.3 Properties of Cancer Cells
6.2.4 Causes of Cancer
6.3 Bioinformatics—A Brief Introduction
6.4 Bioinformatics—A Boon for Cancer Research
6.5 Applications of Bioinformatics Approaches in Cancer
6.5.1 Biomarkers: A Paramount Tool for Cancer Research
6.5.2 Comparative Genomic Hybridization for Cancer Research
6.5.3 Next-Generation Sequencing
6.5.4 miRNA
6.5.5 Microarray Technology
6.5.6 Proteomics-Based Bioinformatics Techniques
6.5.7 Expressed Sequence Tags (EST) and Serial Analysis of Gene Expression (SAGE)
6.6 Bioinformatics: A New Hope for Cancer Therapeutics
6.7 Conclusion
References
7. Biomedical Big Data Analytics Using IoT in Health Informatics
Pawan Singh Gangwar and Yasha Hasija
7.1 Introduction
7.2 Biomedical Big Data
7.2.1 Big EHR Data
7.2.2 Medical Imaging Data
7.2.3 Clinical Text Mining Data
7.2.4 Big OMICs Data
7.3 Healthcare Internet of Things (IoT)
7.3.1 IoT Architecture
7.3.2 IoT Data Source
7.3.2.1 IoT Hardware
7.3.2.2 IoT Middleware
7.3.2.3 IoT Presentation
7.3.2.4 IoT Software
7.3.2.5 IoT Protocols
7.4 Studies Related to Big Data Analytics in Healthcare IoT
7.5 Challenges for Medical IoT & Big Data in Healthcare
7.6 Conclusion
References
8. Statistical Image Analysis of Drying Bovine Serum Albumin Droplets in Phosphate Buffered Saline
Anusuya Pal, Amalesh Gope and Germano S. Iannacchione
8.1 Introduction
8.2 Experimental Methods
8.3 Results
8.3.1 Temporal Study of the Drying Droplets
8.3.2 FOS Characterization of the Drying Evolution
8.3.3 GLCM Characterization of the Drying Evolution
8.4 Discussions
8.4.1 Qualitative Analysis of the Drying Droplets and the Dried Films
8.4.2 Quantitative Analysis of the Drying Droplets and the Dried Films
8.5 Conclusions
Acknowledgments
References
9. Introduction to Deep Learning in Health Informatics
Monika Jyotiyana and Nishtha Kesswani
9.1 Introduction
9.1.1 Machine Learning v/s Deep Learning
9.1.2 Neural Networks and Deep Learning
9.1.3 Deep Learning Architecture
9.1.3.1 Deep Neural Networks
9.1.3.2 Convolutional Neural Networks
9.1.3.3 Deep Belief Networks
9.1.3.4 Recurrent Neural Networks
9.1.3.5 Deep Auto-Encoder
9.1.4 Applications
9.2 Deep Learning in Health Informatics
9.2.1 Medical Imaging
9.2.1.1 CNN v/s Medical Imaging
9.2.1.2 Tissue Classification
9.2.1.3 Cell Clustering
9.2.1.4 Tumor Detection
9.2.1.5 Brain Tissue Classification
9.2.1.6 Organ Segmentation
9.2.1.7 Alzheimer’s and Other NDD Diagnosis
9.3 Medical Informatics
9.3.1 Data Mining
9.3.2 Prediction of Disease
9.3.3 Human Behavior Monitoring
9.4 Bioinformatics
9.4.1 Cancer Diagnosis
9.4.2 Gene Variants
9.4.3 Gene Classification or Gene Selection
9.4.4 Compound–Protein Interaction
9.4.5 DNA–RNA Sequences
9.4.6 Drug Designing
9.5 Pervasive Sensing
9.5.1 Human Activity Monitoring
9.5.2 Anomaly Detection
9.5.3 Biological Parameter Monitoring
9.5.4 Hand Gesture Recognition
9.5.5 Sign Language Recognition
9.5.6 Food Intake
9.5.7 Energy Expenditure
9.5.8 Obstacle Detection
9.6 Public Health
9.6.1 Lifestyle Diseases
9.6.2 Predicting Demographic Information
9.6.3 Air Pollutant Prediction
9.6.4 Infectious Disease Epidemics
9.7 Deep Learning Limitations and Challenges in Health Informatics
References
10. Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review
Shikha Gupta, Nitish Mehndiratta, Swarnim Sinha, Sangana Chaturvedi and Mehak Singla
10.1 Introduction
10.2 Techniques and Algorithms Applied
10.3 Analysis of Major Health Disorders Through Different Techniques
10.3.1 Alzheimer
10.3.2 Dementia
10.3.3 Depression
10.3.4 Schizophrenia and Bipolar Disorders
10.4 Conclusion
References
11. Deep Learning Applications in Medical Image Analysis
Ananya Singha, Rini Smita Thakur and Tushar Patel
11.1 Introduction
11.1.1 Medical Imaging
11.1.2 Artificial Intelligence and Deep Learning
11.1.3 Processing in Medical Images
11.2 Deep Learning Models and its Classification
11.2.1 Supervised Learning
11.2.1.1 RNN (Recurrent Neural Network)
11.2.2 Unsupervised Learning
11.2.2.1 Stacked Auto Encoder (SAE)
11.2.2.2 Deep Belief Network (DBN)
11.2.2.3 Deep Boltzmann Machine (DBM)
11.2.2.4 Generative Adversarial Network (GAN)
11.3 Convolutional Neural Networks (CNN)—A Popular Supervised Deep Model 11.3.1 Architecture of CNN
11.3.2 Learning of CNNs
11.3.3 Medical Image Denoising using CNNs
11.3.4 Medical Image Classification Using CNN
11.4 Deep Learning Advancements—A Biological Overview
11.4.1 Sub-Cellular Level
11.4.2 Cellular Level
11.4.3 Tissue Level
11.4.4 Organ Level
11.4.4.1 The Brain and Neural System
11.4.4.2 Sensory Organs--The Eye and Ear
11.4.4.3 Thoracic Cavity
11.4.4.4 Abdomen and Gastrointestinal (GI) Track
11.4.4.5 Other Miscellaneous Applications
11.5 Conclusion and Discussion
References
12. Role of Medical Image Analysis in Oncology
Gaganpreet Kaur, Hardik Garg, Kumari Heena, Lakhvir Singh, Navroz Kaur, Shubham Kumar and Shadab Alam
12.1 Introduction
12.2 Cancer
12.2.1 Types of Cancer
12.2.2 Causes of Cancer
12.2.3 Stages of Cancer
12.2.4 Prognosis
12.3 Medical Imaging
12.3.1 Anatomical Imaging
12.3.2 Functional Imaging
12.3.3 Molecular Imaging
12.4 Diagnostic Approaches for Cancer
12.4.1 Conventional Approaches
12.4.1.1 Laboratory Diagnostic Techniques
12.4.1.2 Tumor Biopsies
12.4.1.3 Endoscopic Exams
12.4.2 Modern Approaches
12.4.2.1 Image Processing
12.4.2.2 Implications of Advanced Techniques
12.4.2.3 Imaging Techniques
12.5 Conclusion
References
13. A Comparative Analysis of Classifiers Using Particle Swarm Optimization-Based Feature Selection
Chandra Sekhar Biswal, Subhendu Kumar Pani and Sujata Dash
13.1 Introduction
13.2 Feature Selection for Classification
13.2.1 An Overview: Data Mining
13.2.2 Classification Prediction
13.2.3 Dimensionality Reduction
13.2.4 Techniques of Feature Selection
13.2.5 Feature Selection: A Survey
13.2.6 Summary
13.3 Use of WEKA Tool
13.3.1 WEKA Tool
13.3.2 Classifier Selection
13.3.3 Feature Selection Algorithms in WEKA
13.3.4 Performance Measure
13.3.5 Dataset Description
13.3.6 Experiment Design
13.3.7 Results Analysis
13.3.8 Summary
13.4 Conclusion and Future Work
13.4.1 Summary of the Work
13.4.2 Research Challenges
13.4.3 Future Work
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page