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E-raamat: Biomedical Data Mining for Information Retrieval: Methodologies, Techniques, and Applications

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Biomedical Data Mining for Information Retrieval: Methodologies, Techniques, and ApplicationsSuurem pilt
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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 patients 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.
Preface xv
1 Mortality Prediction of ICU Patients Using Machine Learning Techniques 1(20)
Babita Majhi
Aarti Kashyap
Ritanjali Majhi
1.1 Introduction
2(1)
1.2 Review of Literature
3(5)
1.3 Materials and Methods
8(7)
1.3.1 Dataset
8(1)
1.3.2 Data Pre-Processing
8(1)
1.3.3 Normalization
8(2)
1.3.4 Mortality Prediction
10(1)
1.3.5 Model Description and Development
11(4)
1.4 Result and Discussion
15(1)
1.5 Conclusion
16(1)
1.6 Future Work
16(1)
References
17(4)
2 Artificial Intelligence in Bioinformatics 21(32)
V. Samuel Raj
Anjali Priyadarshini
Manoj Kumar Yadav
Ramendra Pati Pandey
Archana Gupta
Arpana Vibhuti
2.1 Introduction
21(1)
2.2 Recent Trends in the Field of AI in Bioinformatics
22(4)
2.2.1 DNA Sequencing and Gene Prediction Using Deep Learning
24(2)
2.3 Data Management and Information Extraction
26(1)
2.4 Gene Expression Analysis
26(4)
2.4.1 Approaches for Analysis of Gene Expression
27(2)
2.4.2 Applications of Gene Expression Analysis
29(1)
2.5 Role of Computation in Protein Structure Prediction
30(1)
2.6 Application in Protein Folding Prediction
31(7)
2.7 Role of Artificial Intelligence in Computer-Aided Drug Design
38(4)
2.8 Conclusions
42(1)
References
43(10)
3 Predictive Analysis in Healthcare Using Feature Selection 53(50)
Aneri Acharya
Jitali Patel
Jigna Patel
3.1 Introduction
54(4)
3.1.1 Overview and Statistics About the Disease
54(2)
3.1.1.1 Diabetes
54(1)
3.1.1.2 Hepatitis
55(1)
3.1.2 Overview of the Experiment Carried Out
56(2)
3.2 Literature Review
58(12)
3.2.1 Summary
58(3)
3.2.2 Comparison of Papers for Diabetes and Hepatitis Dataset
61(9)
3.3 Dataset Description
70(3)
3.3.1 Diabetes Dataset
70(1)
3.3.2 Hepatitis Dataset
71(2)
3.4 Feature Selection
73(3)
3.4.1 Importance of Feature Selection
74(1)
3.4.2 Difference Between Feature Selection, Feature Extraction and Dimensionality Reduction
74(1)
3.4.3 Why Traditional Feature Selection Techniques Still Holds True?
75(1)
3.4.4 Advantages and Disadvantages of Feature Selection Technique
76(1)
3.4.4.1 Advantages
76(1)
3.4.4.2 Disadvantage
76(1)
3.5 Feature Selection Methods
76(8)
3.5.1 Filter Method
76(4)
3.5.1.1 Basic Filter Methods
77(1)
3.5.1.2 Correlation Filter Methods
77(1)
3.5.1.3 Statistical & Ranking Filter Methods
78(2)
3.5.1.4 Advantages and Disadvantages of Filter Method
80(1)
3.5.2 Wrapper Method
80(4)
3.5.2.1 Advantages and Disadvantages of Wrapper Method
82(1)
3.5.2.2 Difference Between Filter Method and Wrapper Method
82(2)
3.6 Methodology
84(1)
3.6.1 Steps Performed
84(1)
3.6.2 Flowchart
84(1)
3.7 Experimental Results and Analysis
85(11)
3.7.1 Task 1-Application of Four Machine Learning Models
85(1)
3.7.2 Task 2-Applying Ensemble Learning Algorithms
86(1)
3.7.3 Task 3-Applying Feature Selection Techniques
87(7)
3.7.4 Task 4-Appling Data Balancing Technique
94(2)
3.8 Conclusion
96(3)
References
99(4)
4 Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications 103(28)
Deepanshu Bajaj
Bharat Bhushan
Divya Yadav
4.1 Introduction
104(1)
4.2 Basic Architecture and Components of e-Health Architecture
105(3)
4.2.1 Front End Layer
106(1)
4.2.2 Communication Layer
107(1)
4.2.3 Back End Layer
107(1)
4.3 Security Requirements in Healthcare 4.0
108(5)
4.3.1 Mutual-Authentications
109(1)
4.3.2 Anonymity
110(1)
4.3.3 Un-Traceability
111(1)
4.3.4 Perfect-Forward-Secrecy
111(1)
4.3.5 Attack Resistance
111(2)
4.3.5.1 Replay Attack
111(1)
4.3.5.2 Spoofing Attack
112(1)
4.3.5.3 Modification Attack
112(1)
4.3.5.4 MITM Attack
112(1)
4.3.5.5 Impersonation Attack
112(1)
4.4 ICT Pillar's Associated With HC4.0
113(8)
4.4.1 IoT in Healthcare 4.0
114(1)
4.4.2 Cloud Computing (CC) in Healthcare 4.0
115(1)
4.4.3 Fog Computing (FC) in Healthcare 4.0
116(1)
4.4.4 BigData (BD) in Healthcare 4.0
117(1)
4.4.5 Machine Learning (ML) in Healthcare 4.0
118(2)
4.4.6 Blockchain (BC) in Healthcare 4.0
120(1)
4.5 Healthcare 4.0's Applications-Scenarios
121(5)
4.5.1 Monitor-Physical and Pathological Related Signals
121(3)
4.5.2 Self-Management, and Wellbeing Monitor, and its Precaution
124(1)
4.5.3 Medication Consumption Monitoring and Smart-Pharmaceutics
124(1)
4.5.4 Personalized (or Customized) Healthcare
125(1)
4.5.5 Cloud-Related Medical Information's Systems
125(1)
4.5.6 Rehabilitation
126(1)
4.6 Conclusion
126(1)
References
127(4)
5 Improved Social Media Data Mining for Analyzing Medical Trends 131(32)
Minakshi Sharma
Sunil Sharma
5.1 Introduction
132(4)
5.1.1 Data Mining
132(1)
5.1.2 Major Components of Data Mining
132(2)
5.1.3 Social Media Mining
134(1)
5.1.4 Clustering in Data Mining
134(2)
5.2 Literature Survey
136(4)
5.3 Basic Data Mining Clustering Technique
140(7)
5.3.1 Classifier and Their Algorithms in Data Mining
143(4)
5.4 Research Methodology
147(4)
5.5 Results and Discussion
151(6)
5.5.1 Tool Description
151(1)
5.5.2 Implementation Results
152(4)
5.5.3 Comparison Graphs Performance Comparison
156(1)
5.6 Conclusion & Future Scope
157(1)
References
158(5)
6 Bioinformatics: An Important Tool in Oncology 163(34)
Gaganpreet Kaur
Saurabh Gupta
Gagandeep Kaur
Manju Verma
Pawandeep Kaur
6.1 Introduction
164(1)
6.2 Cancer-A Brief Introduction
165(4)
6.2.1 Types of Cancer
166(1)
6.2.2 Development of Cancer
166(1)
6.2.3 Properties of Cancer Cells
166(2)
6.2.4 Causes of Cancer
168(1)
6.3 Bioinformatics-A Brief Introduction
169(1)
6.4 Bioinformatics-A Boon for Cancer Research
170(4)
6.5 Applications of Bioinformatics Approaches in Cancer
174(14)
6.5.1 Biomarkers: A Paramount Tool for Cancer Research
175(2)
6.5.2 Comparative Genomic Hybridization for Cancer Research
177(1)
6.5.3 Next-Generation Sequencing
178(1)
6.5.4 miRNA
179(2)
6.5.5 Microarray Technology
181(4)
6.5.6 Proteomics-Based Bioinformatics Techniques
185(2)
6.5.7 Expressed Sequence Tags (EST) and Serial Analysis of Gene Expression (SAGE)
187(1)
6.6 Bioinformatics: A New Hope for Cancer Therapeutics
188(3)
6.7 Conclusion
191(1)
References
192(5)
7 Biomedical Big Data Analytics Using IoT in Health Informatics 197(16)
Pawan Singh Gangwar
Yasha Hasija
7.1 Introduction
198(2)
7.2 Biomedical Big Data
200(2)
7.2.1 Big EHR Data
201(1)
7.2.2 Medical Imaging Data
201(1)
7.2.3 Clinical Text Mining Data
201(1)
7.2.4 Big OMICs Data
202(1)
7.3 Healthcare Internet of Things (IoT)
202(4)
7.3.1 IoT Architecture
202(2)
7.3.2 IoT Data Source
204(13)
7.3.2.1 IoT Hardware
204(1)
7.3.2.2 IoT Middleware
205(1)
7.3.2.3 IoT Presentation
205(1)
7.3.2.4 IoT Software
205(1)
7.3.2.5 IoT Protocols
206(1)
7.4 Studies Related to Big Data Analytics in Healthcare IoT
206(3)
7.5 Challenges for Medical IoT & Big Data in Healthcare
209(1)
7.6 Conclusion
210(1)
References
210(3)
8 Statistical Image Analysis of Drying Bovine Serum Albumin Droplets in Phosphate Buffered Saline 213(24)
Anusuya Pal
Amalesh Gope
Germano S. Iannacchione
8.1 Introduction
214(2)
8.2 Experimental Methods
216(1)
8.3 Results
217(7)
8.3.1 Temporal Study of the Drying Droplets
217(2)
8.3.2 FOS Characterization of the Drying Evolution
219(1)
8.3.3 GLCM Characterization of the Drying Evolution
220(4)
8.4 Discussions
224(7)
8.4.1 Qualitative Analysis of the Drying Droplets and the Dried Films
224(3)
8.4.2 Quantitative Analysis of the Drying Droplets and the Dried Films
227(4)
8.5 Conclusions
231(1)
Acknowledgments
232(1)
References
232(5)
9 Introduction to Deep Learning in Health Informatics 237(26)
Monika Jyotiyana
Nishtha Kesswani
9.1 Introduction
237(9)
9.1.1 Machine Learning v/s Deep Learning
240(1)
9.1.2 Neural Networks and Deep Learning
241(1)
9.1.3 Deep Learning Architecture
242(4)
9.1.3.1 Deep Neural Networks
243(1)
9.1.3.2 Convolutional Neural Networks
243(1)
9.1.3.3 Deep Belief Networks
244(1)
9.1.3.4 Recurrent Neural Networks
244(1)
9.1.3.5 Deep Auto-Encoder
245(1)
9.1.4 Applications
246(1)
9.2 Deep Learning in Health Informatics
246(3)
9.2.1 Medical Imaging
246(3)
9.2.1.1 CNN v/s Medical Imaging
247(1)
9.2.1.2 Tissue Classification
247(1)
9.2.1.3 Cell Clustering
247(1)
9.2.1.4 Tumor Detection
247(1)
9.2.1.5 Brain Tissue Classification
248(1)
9.2.1.6 Organ Segmentation
248(1)
9.2.1.7 Alzheimer's and Other NDD Diagnosis
248(1)
9.3 Medical Informatics
249(1)
9.3.1 Data Mining
249(1)
9.3.2 Prediction of Disease
249(1)
9.3.3 Human Behavior Monitoring
250(1)
9.4 Bioinformatics
250(2)
9.4.1 Cancer Diagnosis
250(1)
9.4.2 Gene Variants
251(1)
9.4.3 Gene Classification or Gene Selection
251(1)
9.4.4 Compound-Protein Interaction
251(1)
9.4.5 DNA-RNA Sequences
252(1)
9.4.6 Drug Designing
252(1)
9.5 Pervasive Sensing
252(3)
9.5.1 Human Activity Monitoring
253(1)
9.5.2 Anomaly Detection
253(1)
9.5.3 Biological Parameter Monitoring
253(1)
9.5.4 Hand Gesture Recognition
253(1)
9.5.5 Sign Language Recognition
254(1)
9.5.6 Food Intake
254(1)
9.5.7 Energy Expenditure
254(1)
9.5.8 Obstacle Detection
254(1)
9.6 Public Health
255(2)
9.6.1 Lifestyle Diseases
255(1)
9.6.2 Predicting Demographic Information
256(1)
9.6.3 Air Pollutant Prediction
256(1)
9.6.4 Infectious Disease Epidemics
257(1)
9.7 Deep Learning Limitations and Challenges in Health Informatics
257(1)
References
258(5)
10 Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review 263(30)
Shikha Gupta
Nitish Mehndiratta
Swarnim Sinha
Sangana Chaturvedi
Mehak Singla
10.1 Introduction
263(2)
10.2 Techniques and Algorithms Applied
265(2)
10.3 Analysis of Major Health Disorders Through Different Techniques
267(18)
10.3.1 Alzheimer
267(1)
10.3.2 Dementia
268(6)
10.3.3 Depression
274(7)
10.3.4 Schizophrenia and Bipolar Disorders
281(4)
10.4 Conclusion
285(1)
References
286(7)
11 Deep Learning Applications in Medical Image Analysis 293(58)
Ananya Singha
Rini Smita Thakur
Tushar Patel
11.1 Introduction
294(9)
11.1.1 Medical Imaging
295(1)
11.1.2 Artificial Intelligence and Deep Learning
296(4)
11.1.3 Processing in Medical Images
300(3)
11.2 Deep Learning Models and its Classification
303(6)
11.2.1 Supervised Learning
303(1)
11.2.1.1 RNN (Recurrent Neural Network)
303(1)
11.2.2 Unsupervised Learning
304(5)
11.2.2.1 Stacked Auto Encoder (SAE)
304(2)
11.2.2.2 Deep Belief Network (DBN)
306(1)
11.2.2.3 Deep Boltzmann Machine (DBM)
307(1)
11.2.2.4 Generative Adversarial Network (GAN)
308(1)
11.3 Convolutional Neural Networks (CNN)-A Popular Supervised Deep Model
309(8)
11.3.1 Architecture of CNN
310(3)
11.3.2 Learning of CNNs
313(1)
11.3.3 Medical Image Denoising using CNNs
314(2)
11.3.4 Medical Image Classification Using CNN
316(1)
11.4 Deep Learning Advancements-A Biological Overview
317(18)
11.4.1 Sub-Cellular Level
317(2)
11.4.2 Cellular Level
319(4)
11.4.3 Tissue Level
323(3)
11.4.4 Organ Level
326(27)
11.4.4.1 The Brain and Neural System
326(3)
11.4.4.2 Sensory Organs-The Eye and Ear
329(1)
11.4.4.3 Thoracic Cavity
330(1)
11.4.4.4 Abdomen and Gastrointestinal (GI) Track
331(1)
11.4.4.5 Other Miscellaneous Applications
332(3)
11.5 Conclusion and Discussion
335(1)
References
336(15)
12 Role of Medical Image Analysis in Oncology 351(32)
Gaganpreet Kaur
Hardik Garg
Kumari Heena
Lakhvir Singh
Navroz Kaur
Shubham Kumar
Shadab Alam
12.1 Introduction
352(1)
12.2 Cancer
353(4)
12.2.1 Types of Cancer
354(1)
12.2.2 Causes of Cancer
355(1)
12.2.3 Stages of Cancer
355(1)
12.2.4 Prognosis
356(1)
12.3 Medical Imaging
357(1)
12.3.1 Anatomical Imaging
357(1)
12.3.2 Functional Imaging
358(1)
12.3.3 Molecular Imaging
358(1)
12.4 Diagnostic Approaches for Cancer
358(17)
12.4.1 Conventional Approaches
358(3)
12.4.1.1 Laboratory Diagnostic Techniques
359(1)
12.4.1.2 Tumor Biopsies
359(1)
12.4.1.3 Endoscopic Exams
360(1)
12.4.2 Modern Approaches
361(24)
12.4.2.1 Image Processing
361(1)
12.4.2.2 Implications of Advanced Techniques
362(1)
12.4.2.3 Imaging Techniques
363(12)
12.5 Conclusion
375(1)
References
376(7)
13 A Comparative Analysis of Classifiers Using Particle Swarm Optimization-Based Feature Selection 383(26)
Chandra Sekhar Biswal
Subhendu Kumar Pani
Sujata Dash
13.1 Introduction
384(1)
13.2 Feature Selection for Classification
385(10)
13.2.1 An Overview: Data Mining
385(2)
13.2.2 Classification Prediction
387(1)
13.2.3 Dimensionality Reduction
387(1)
13.2.4 Techniques of Feature Selection
388(4)
13.2.5 Feature Selection: A Survey
392(2)
13.2.6 Summary
394(1)
13.3 Use of WEKA Tool
395(6)
13.3.1 WEKA Tool
395(1)
13.3.2 Classifier Selection
395(1)
13.3.3 Feature Selection Algorithms in WEKA
395(1)
13.3.4 Performance Measure
396(2)
13.3.5 Dataset Description
398(1)
13.3.6 Experiment Design
398(1)
13.3.7 Results Analysis
399(2)
13.3.8 Summary
401(1)
13.4 Conclusion and Future Work
401(3)
13.4.1 Summary of the Work
401(1)
13.4.2 Research Challenges
402(2)
13.4.3 Future Work
404(1)
References
404(5)
Index 409
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. Ajiths 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+.
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