Big Data Analytics for Healthcare

Datasets, Techniques, Life Cycles, Management, and Applications

1st Edition - May 19, 2022
Editor: Pantea Keikhosrokiani
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 9 0 7 - 4
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 8 5 1 6 - 1

Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on the… Read more

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Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Section I. Theories and concepts of big data analytics in healthcare
Chapter 1. Big data analytics in healthcare: theory, tools, techniques and its applications
1. Introduction
2. Challenges in big data analytics
3. Data analytics life cycle
4. Data analytics during the Covid-19 pandemic
5. Big data tools in healthcare
6. Summary
Chapter 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
Chapter 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
3. Accountancy of big data analytics in health care domains
4. Machine learning based on big data analytics in real time: autism disease diagnosis
5. Open source tools: cloud resources for health care management
6. Conclusion
Chapter 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
Section II. Big medical data: Techniques, managements, and applications
Chapter 5. Big data analytics techniques for healthcare
1. Introduction
2. Big data in healthcare
3. Characteristics of big data in healthcare
4. Key elements of big data analysis
5. Big data analytical tools used in healthcare
6. Conclusions
Chapter 6. Big data analytics in precision medicine
1. Introduction
2. Biomedical big data
3. Challenges associated with big data
4. Machine learning techniques for big data analytics
5. Methodology
6. Applications
7. Conclusion
Chapter 7. Recent advances in processing, interpreting, and managing biological data for therapeutic intervention of human infectious disease
1. Introduction
2. Biological data capturing and processing
3. Interpretation of processed clinical data
4. Patients' data management for digital therapeutics
5. Advantages and limitations
6. Conclusion and future direction
Chapter 8. Big data analytics for health: a comprehensive review of techniques and applications
1. Introduction
2. Literature review
3. Discussion
4. Conclusions
Section III. Diagnosis and treatment: Big data analytical techniques, datasets, life cycles, managements, and applications for diagnosis and treatment
Chapter 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
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
Chapter 10. Big medical data analytics for diagnosis
1. Introduction
2. Big medical data analytics in disease diagnosis
3. Big medical data analytics tools/algorithms
4. Challenges
5. Future scopes
6. Conclusion
Chapter 11. Big data analytics and radiomics to discover diagnostics on different cancer types
1. Introduction
2. Radiomics
3. The methodology of radiomics
4. The applications of radiomics on several kinds of cancer types
5. 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
Chapter 12. Big medical data, cloud computing, and artificial intelligence for improving diagnosis in healthcare
1. Introduction
2. Retrieving patient data from medical apps
3. Collecting patient data into cloud-based big data repositories
4. Using artificial intelligence techniques for improving diagnosis
5. Conclusions
Section IV. Prediction: Big data analytical techniques, datasets, life cycles, managements, and applications for prediction
Chapter 13. Use of artificial intelligence for predicting infectious disease
1. Introduction
2. Mathematical modeling of infectious diseases and their development
3. Predicting infectious diseases using artificial intelligence
4. Conclusion
Chapter 14. Hospital data analytics system for tracking and predicting obese patients' lifestyle habits
1. Introduction
2. Related works
3. Development methodology
4. System design and implementation
5. Data analytics, results, and user interface
6. Discussion and conclusion
Chapter 15. Predictions on diabetic patient datasets using big data analytics and machine learning techniques
1. Introduction
2. Big data analytics using mapreduce, Pig, Hive, and Spark
3. Methodology adopted
4. Conclusion
Chapter 16. Skin cancer prediction using big data analytics and AI techniques
1. Introduction
2. Hadoop
3. Spark
4. Literature review
5. Methodology
6. Data visualization and analysis
7. Results and discussion
8. Conclusion
Section V. Big medical fake news analytics for preventing medical misinformation and myths
Chapter 17. COVID-19 fake news analytics from social media using topic modeling and clustering
1. Introduction
2. Background and related work
3. Methodology
4. Data analysis and results (COVID-19 news classification)
5. Conclusion
Chapter 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
5. Data analytics and user interface
6. System testing and evaluation
7. Conclusion
Section VI. Challenges and future of big data in healthcare
Chapter 19. Privacy security risks of big data processing in healthcare
1. Introduction
2. Related work
3. Methodology
4. Results
5. Conclusion
Chapter 20. Opportunities and challenges in healthcare with the management of big biomedical data
1. Introduction
2. Biomedical data types and role of machine learning
3. Current big data challenges in healthcare
4. Healthcare data management and its limitations
5. Conclusion
Chapter 21. Future direction for healthcare based on big data analytics
1. Introduction
2. Theoretical framework
3. Empirical methodological approach
4. Discussion
5. Implications and future research
6. Conclusions
Annex 1
Section VII. Case studies of big data in healthcare arena
Chapter 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 orthopedics research
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
Chapter 23. Predicting onset (type-2) of diabetes from medical records using binary class classification
1. Introduction
2. Paper review
3. Proposed methodology
4. Result and discussion
5. Conclusion
Chapter 24. Screening programs incorporating big data analytics
1. Introduction: disease screening and screening programs
2. Evidence-based medicine for big data analytics–facilitated screening programs
3. Screening programs incorporating big data analytics
4. Challenges of big data–acilitated screening programs
5. Conclusions: toward next generation big data analytics–facilitated disease screening
Index

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Pantea Keikhosrokiani