Big Data Analytics for Healthcare: Datasets, Techniques, Life Cycles, Management, and Applications

Big Data Analytics for Healthcare: Datasets, Techniques, Life Cycles,
Management, and Applications
Copyright
Contributors
Preface
1. Overview of big medical data and analytical infrastructure for decision-making in healthcare: healthcare intelligence
2. Purpose of the book
3. Organization of the book
3.1 Section I: Theories and concepts of big data analytics in healthcare
3.2 Section II: Big medical data: Techniques, managements, and applications
3.3 Section III: Diagnosis and treatment: Big data analytical techniques, datasets, life cycles, managements, and applications ...
3.4 Section IV: Prediction: Big data analytical techniques, datasets, life cycles, managements, and applications for prediction
3.5 Section V: Big medical fake news analytics for preventing medical misinformation and myths
3.6 Section VI: Challenges and future of big data in healthcare
3.7 Section VII: Case studies of big data in healthcare arena
References
1. Big data analytics in healthcare: theory, tools, techniques and its applications
1. Introduction
1.1 Volume
1.1.1 Impact of increasing volume in healthcare data
1.2 Velocity
1.2.1 Impact of increasing velocity in healthcare data
1.3 Variety
1.3.1 Impact of increasing variety in healthcare data
1.4 Other factors influencing in big data
2. Challenges in big data analytics
2.1 Management of big data
2.2 Security and privacy concerns
2.3 Scalability
3. Data analytics life cycle
3.1 Data generation
3.1.1 Structured data
3.1.2 Unstructured data
3.1.3 Semistructured data
3.1.4 Quasi structured data
3.2 Big data acquisition in healthcare
4. Data analytics during the Covid-19 pandemic
5. Big data tools in healthcare
5.1 Big data management and analysis
6. Summary
References
Further reading
2. Driving impact through big data utilization and analytics in the context of a Learning Health System
1. Introduction
2. What matters for healthcare?
3. Global strategies for impact on health
4. What is big data?
5. Applying big data—precision medicine
6. Learning Health System—a paradigm for the future?
7. Driving big data utilization in an LHS
8. Challenges
9. Conclusion
Funding statement
References
3. Classification of medical big data: a review of systematic analysis of medical big data in real-time setup
1. Introduction
2. Types of data
2.1 Attributes of big data
2.2 Core big data analysis strategies
2.2.1 A or B testing
2.2.2 Unification of data
2.2.3 Data feature extraction or mining of data
2.2.4 Artificial intelligence and machine learning
2.2.5 Math and statistics
3. Accountancy of big data analytics in health care domains
3.1 Hospital and health hubs
3.2 Malignancy detection using big data analytics
3.3 Medical intelligence
3.4 E-health records
3.5 E-medicine
3.6 Evolution of new medicines and practices in healthcare
4. Machine learning based on big data analytics in real time: autism disease diagnosis
5. Open source tools: cloud resources for health care management
5.1 Apache “Hadoop”
5.1.1 HDFS or “Hadoop” distributed file system
6. Conclusion
References
4. Towards big data framework in government public open data (GPOD) for health
1. Introduction
2. Related works
3. Methodology
4. THE finding
5. Contribution, limitation, and discussion
6. Conclusion
References
Further reading
5. Big data analytics techniques for healthcare
1. Introduction
2. Big data in healthcare
2.1 Structured data
2.2 Unstructured data
2.3 Semistructured data
2.4 Genomic data
2.5 Sentiment data
2.6 Clinical data
3. Characteristics of big data in healthcare
3.1 Volume
3.2 Velocity
3.3 Variety
3.4 Value
3.5 Variability
3.6 Veracity
4. Key elements of big data analysis
4.1 Data inputs
4.2 Functional elements
4.2.1 Data preparation and processing
4.2.2 Building analytical model
4.2.3 Decision support tools and data visualization
4.3 Human element
4.4 Security element
4.4.1 Authentication
4.4.2 Encryption
4.4.3 Access control
5. Big data analytical tools used in healthcare
5.1 Hadoop distribution file system
5.2 MapReduce
5.3 Hive
5.4 Pig and PigLatin
5.5 ZooKeeper
5.6 HBase
6. Conclusions
References
6. Big data analytics in precision medicine
1. Introduction
2. Biomedical big data
2.1 Omics data
2.2 Electronic health record data
3. Challenges associated with big data
3.1 Heterogeneous data
3.2 High dimensionality
3.3 Data quality problems
3.4 Frequency of collecting diverse data
4. Machine learning techniques for big data analytics
5. Methodology
5.1 Big data analytics in omics data
5.1.1 Preprocessing of omics data
5.1.1.1 Identification of biomarkers through omics data
5.1.1.2 Utilizing omics data for systems biology models
5.2 Big data analytics in EHR data
5.2.1 Preprocessing of EHR data
5.2.2 EHR data mining
5.3 Enablers for big medical data analytics
6. Applications
6.1 Disease subtyping and biomarker discovery
6.2 Drug repurposing
6.3 Integrating omics data into EHR
7. Conclusion
References
7. Recent advances in processing, interpreting, and managing biological data for therapeutic intervention of human infectious ...
1. Introduction
1.1 Need of big data in therapeutic intervention
2. Biological data capturing and processing
2.1 Architectural framework
2.2 Data modeling
2.3 Maintenance of threshold quality of data
3. Interpretation of processed clinical data
3.1 Qualitative approach
3.2 Quantitative approach
4. Patients' data management for digital therapeutics
5. Advantages and limitations
6. Conclusion and future direction
Acknowledgments
References
8. Big data analytics for health: a comprehensive review of techniques and applications
1. Introduction
2. Literature review
2.1 Methodology
2.2 Dimensions of big data in health
2.3 Big data–based research in health
2.4 Big data analytics applications for health
3. Discussion
3.1 Opportunities and challenges
4. Conclusions
Acknowledgments
References
9. Recent applications of data mining in medical diagnosis and prediction
1. Introduction
2. Big data and the health sector
3. A machine learning medical diagnosis model based on patients' complaints
4. An early prediction and diagnosis of sepsis in intensive care units
5. A machine learning approach to predict creatine kinase test results
5.1 Model creation
6. Use of artificial intelligence in the prediction of malignant potential of gastric gastrointestinal stromal tumors
7. Weekly emotional changes amidst Covid-19: Turkish experience
8. Conclusion
References
10. Big medical data analytics for diagnosis
1. Introduction
1.1 Big medical data
1.2 Data lifecycle
1.2.1 Data Assortment
1.2.2 Data prepreparing
1.2.3 Data declination and conversion
1.2.4 Data analytics
1.2.5 Data output
1.3 Impact of big medical data on the healthcare system
1.4 Digitized big medical data analytical applications for the health industry
1.4.1 Cancer and genomics treatment
1.4.2 Monitoring of patient vitals
1.4.3 Healthcare intelligence
1.4.4 Prevention and detection of frauds
2. Big medical data analytics in disease diagnosis
2.1 Cardiology
2.1.1 Big medical data analytics in heart attack prediction
2.1.2 Troponin and implanted sensors for heart attack prediction
2.1.3 Wireless detection
2.1.4 Telecardiology in heart attack prediction
2.2 Neurology
2.2.1 Epilepsy and epileptic seizure diagnosis
2.2.2 Parkinson's disease diagnosis
2.2.3 Multiple sclerosis diagnosis
2.2.4 Stroke
2.3 Ophthalmology
2.3.1 Deep learning in ophthalmology
2.3.2 Diabetic retinopathy
2.3.3 Age-related macular degeneration
2.3.4 Glaucoma
2.4 Respiratory system
2.4.1 Big medical data in lung care purpose
2.4.2 Use of big medical data analytics to interpret COPD
2.4.3 Practice of big medical data in lung cancer
2.4.4 Solicitation of big medical data to regulate the controlling of COVID-19 pandemic
3. Big medical data analytics tools/algorithms
3.1 Machine learning on big medical data analysis for diagnosis
3.2 Data mining in big medical data analytics
3.3 The Internet of Things and disease prediction
4. Challenges
5. Future scopes
6. Conclusion
References
11. Big data analytics and radiomics to discover diagnostics on different cancer types
1. Introduction
2. Radiomics
3. The methodology of radiomics
3.1 Image acquisition
3.2 Segmentation
3.3 Feature extraction
3.4 Analysis
4. The applications of radiomics on several kinds of cancer types
5. Big data
5.1 Structured data
5.2 Semistructured data
5.3 Unstructured data
5.4 The components of big data
6. Big data analytics
7. The similarities and differences of radiomics and big data analytics
8. The challenges of radiomics and big data analytics
9. The relationship between radiomics and big data analytics
10. Discussion
11. Conclusion
References
12. Big medical data, cloud computing, and artificial intelligence for improving diagnosis in healthcare
1. Introduction
2. Retrieving patient data from medical apps
2.1 Medical apps for cutaneous disorders
2.2 Medical apps for cardiovascular diseases
2.3 Medical apps for visual and cognitive disorders
2.4 New methods in apps for testing blood pressure and blood glucose levels
3. Collecting patient data into cloud-based big data repositories
3.1 Big data repositories in healthcare
3.1.1 IoT in healthcare
3.2 Management and analysis of big data in healthcare
3.3 Commercial platforms for healthcare data analytics
3.3.1 Linguamatics
3.3.2 IBM Watson
4. Using artificial intelligence techniques for improving diagnosis
4.1 Clinical Information Systems and clinical decision support systems
4.2 AI methods used in healthcare
4.2.1 Machine learning, neural networks, and deep learning in healthcare
4.2.2 Natural language processing in healthcare
4.2.3 Process mining and pathway identification in healthcare
4.2.4 Rule-based expert systems in healthcare
4.2.5 Robots in healthcare
4.2.6 Robotic process automation in healthcare
4.2.7 The use of AI by hospital information systems in decision-making
4.2.7.1 Knowledge-based decision support in AI for healthcare
4.2.7.2 Data-driven decision support in AI for healthcare
4.2.7.3 Probabilistic reasoning in AI for healthcare
5. Conclusions
References
13. Use of artificial intelligence for predicting infectious disease
1. Introduction
2. Mathematical modeling of infectious diseases and their development
2.1 SIR and SEIR
2.2 Agent-based model
3. Predicting infectious diseases using artificial intelligence
3.1 Big data
3.2 AI
3.2.1 Influenza
3.2.2 Skin diseases
3.2.3 Malaria
3.2.4 COVID-19
4. Conclusion
References
14. Hospital data analytics system for tracking and predicting obese patients' lifestyle habits
1. Introduction
2. Related works
2.1 Existing habit-based healthcare systems with analytical features
2.2 Big data and predictive analytics in healthcare
2.3 Big data and Clinical Decision Support Systems
3. Development methodology
4. System design and implementation
4.1 System modules and use case diagram
4.2 System architecture design and predictive analytics
4.3 Implementation strategy
4.4 Testing strategy
5. Data analytics, results, and user interface
5.1 Dataset
5.2 Predictive analytics using machine learning
5.2.1 Predictive analytical models
5.2.1.1 Holt–Winters forecasting algorithm
5.2.1.2 Holt's linear forecasting model
5.2.1.3 ARIMA model
5.2.2 Prediction module for HDAS
5.2.3 Sample results for predictive analytics in HDAS
5.3 User interface design
6. Discussion and conclusion
Acknowledgments
References
15. Predictions on diabetic patient datasets using big data analytics and machine learning techniques
1. Introduction
1.1 Challenges of healthcare data
2. Big data analytics using mapreduce, Pig, Hive, and Spark
2.1 Hadoop MapReduce framework
2.2 Apache Pig
2.3 Apache Hive
2.4 Apache Spark
3. Methodology adopted
3.1 Big data and machine learning techniques for healthcare
3.1.1 K-nearest neighbor
3.1.2 Decision trees
3.1.3 Bagged trees
4. Conclusion
References
16. Skin cancer prediction using big data analytics and AI techniques
1. Introduction
1.1 Dimensions of big data
1.2 Types of big data
1.3 Big data analytics
1.4 Platforms of big data analytics
2. Hadoop
3. Spark
4. Literature review
5. Methodology
5.1 Dataset
5.2 Device/platform used
5.3 Techniques/models/algorithms
5.4 Logistic regression
5.5 Support vector machine
5.6 Gradient boosting
5.7 VGG19
5.8 InceptionResNetV2
5.9 MobileNet SSD
5.10 MelConvo2d
6. Data visualization and analysis
7. Results and discussion
7.1 Model implementation of combination of logistic regression, support vector machine, and gradient boosting
7.2 Model implementation using VGG19 architecture
7.3 Model implementation of InceptionResNetV2
7.4 Model implementation of MobileNet SSD
7.5 Model implementation MelConvo2D
8. Conclusion
References
17. COVID-19 fake news analytics from social media using topic modeling and clustering
1. Introduction
2. Background and related work
2.1 Misinformation and fake news
2.2 Related studies of misinformation and medical fake news on social media related to COVID-19
3. Methodology
3.1 Data collection
3.2 Topic modeling
3.3 Programming tools
4. Data analysis and results (COVID-19 news classification)
4.1 COVID-19 news dataset
4.2 Data cleaning and data preprocessing
4.3 Initial analysis and data exploration
4.4 LDA topic modeling
4.4.1 Topics from LDA model
4.4.2 pyLDAvis interpretation
4.4.3 LDA topic clustering
5. Conclusion
Acknowledgments
References
18. Big medical data mining system (BigMed) for the detection and classification of COVID-19 misinformation
1. Introduction
2. Background and related works
3. Development methodology
4. System design and implementation
4.1 System architecture and module design
4.2 Use case diagram
4.3 System interface design
4.4 Implementation strategy
5. Data analytics and user interface
5.1 Dataset and data preprocessing
5.2 News detection and classification
5.3 Analytical dashboard
6. System testing and evaluation
6.1 Testing strategy
6.1.1 Unit testing
6.1.2 Integration testing
6.1.3 System testing
6.1.4 User acceptance test
7. Conclusion
Acknowledgments
References
19. Privacy security risks of big data processing in healthcare
1. Introduction
2. Related work
2.1 HBD mining
2.2 Privacy issues of HBD
2.3 Solution of the privacy problem of HBD
3. Methodology
3.1 Big data analysis in China's healthcare sector
3.2 Key technologies of HBD mining
3.3 Establishment of indicator system for privacy and security risk assessment of HBD
3.4 Assessment model for privacy security risk
4. Results
4.1 Analysis on the development status of China's medical and healthcare field
4.2 Analysis of medical and healthcare costs of major diseases in China
4.3 Testing of privacy and security risk assessment model for HBD in cloud environment
4.4 Application of parallel random forest algorithm in hospital intelligent guidance
5. Conclusion
Acknowledgment
References
20. Opportunities and challenges in healthcare with the management of big biomedical data
1. Introduction
2. Biomedical data types and role of machine learning
2.1 Electronic health records
2.1.1 Advantages
2.1.2 Other benefits
2.1.2.1 Reformed health care
2.1.2.2 Significant use
2.2 Medical scan images
2.2.1 Computerized medical image analysis in CT scanning
2.2.2 Boosting the speed, power, and comfort of magnetic resonance imaging
2.2.3 PET scans
2.2.4 X-rays
3. Current big data challenges in healthcare
3.1 Big data and healthcare
3.1.1 Cloud computing
3.1.2 Artificial intelligence
3.1.3 Data mining
3.1.4 COVID-19 and data analytics
3.2 Big data challenges in healthcare
3.2.1 Lack of research
3.2.2 Lack of expert skills in handling
4. Healthcare data management and its limitations
4.1 Data interoperability
4.2 Data quality
4.3 Data insecurity
4.4 Policy settings
5. Conclusion
References
21. Future direction for healthcare based on big data analytics
1. Introduction
2. Theoretical framework
2.1 Leadership conceptual framework
2.2 Big data analytics and AI in the health sector
3. Empirical methodological approach
3.1 Results
3.1.1 Profile of the experts
3.1.2 Analysis of the reliability
3.1.3 Data analysis
4. Discussion
5. Implications and future research
5.1 Theoretical implications of the study findings
5.2 Policy implications
5.3 Future research avenues
6. Conclusions
Annex 1
Questionnaire
References
22. Big data in orthopedics: between hypes and hopes
1. Introduction
2. Roles and applications of epidemiological big data in current orthopedics research
3. Roles and applications of molecular big data in current orthopedics research
4. Roles and applications of big data generated by imaging techniques and wearable technologies/smart sensors in current ortho ...
5. Roles and applications of infodemiological big data in current orthopedics research
6. “Participatory orthopedics”: integrating basic and translational orthopedics and citizen science
7. Conclusions and future prospects
References
23. Predicting onset (type-2) of diabetes from medical records using binary class classification
1. Introduction
1.1 Background of the study
1.2 Problem statement, objectives, and scope of the study
2. Paper review
2.1 Defining the field
2.1.1 Onset/type-2 diabetes
2.1.2 Data mining
2.1.3 Data mining task
2.1.4 Data mining-classifications and regression
2.1.5 Data mining-clustering
2.1.6 Data mining-association rules
2.1.7 Data mining-text, link, content
2.2 Related works
3. Proposed methodology
3.1 Model diagram
3.2 Brief description of algorithms used
3.2.1 Decision tree algorithm (REPTree, J48)
3.2.2 Naïve Bayesian (Naive Bayes)
3.2.3 Functions (Logistic, SMO)
3.2.4 Rule based (ZeroR, PART)
3.2.5 LAZY (IBk)
3.3 Data mining tools
3.4 Dataset
3.5 Evaluation measures
4. Result and discussion
4.1 Classifier's performance based on of classified instance
4.2 Confusion matrix
4.3 F-measure
4.4 Precision
4.5 Recall
4.6 Accuracy
4.7 Comparative analysis summary
5. Conclusion
References
24. Screening programs incorporating big data analytics
1. Introduction: disease screening and screening programs
1.1 Goals and measures for screening programs
1.2 Measures and utility of a screening program
1.3 Databases and training sets of screening programs
1.4 Nationwide screening programs
2. Evidence-based medicine for big data analytics–facilitated screening programs
2.1 Principles of evidence-based medicine
2.2 Gap between clinical research and best practices
2.3 Hierarchy and levels of clinical evidence and information
3. Screening programs incorporating big data analytics
3.1 Cancer screening programs in the era of big data analytics
3.1.1 Risk management for patients with cancer
3.1.2 Radiomics
3.1.3 Pathology interpretation
3.2 Diabetes screening programs in the era of big data analytics
3.3 Drug allergy screening in the era of big data analytics
4. Challenges of big data–acilitated screening programs
4.1 Acquisition of high-quality clinical data
4.2 Overdiagnoses in big data–facilitated screening programs
4.3 External validation with prospective studies
4.4 Ensuring representativeness and mitigating bias
5. Conclusions: toward next generation big data analytics–facilitated disease screening
References
Index
A
B
C
D
E
F
G
H
I
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z