Big Data Analytics for Healthcare

Big Data Analytics for Healthcare

Datasets, Techniques, Life Cycles, Management, and Applications

1st Edition - May 19, 2022

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  • Editor: Pantea Keikhosrokiani
  • Paperback ISBN: 9780323919074
  • eBook ISBN: 9780323985161

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Description

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.

Key Features

  • Presents theories, methods and approaches in which data analytic techniques are used for medical data
  • Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases
  • Discusses social, behavioral, and medical fake news analytics for medical information systems

Readership

Graduate students and researchers on medical informatics; Data scientists, computer scientists

Table of Contents

  • Section I. Theories and Concepts of Big Data Analytics in Healthcare
    1. Big data analytics in healthcare: Theory, tools, techniques and its applications
    2. Driving impact through big data utilization and analytics in the context of a learning health system
    3. Classification of medical big data: A review of systematic analysis of medical big data in real time setup
    4. Towards big data framework in government public open data (GPOD) for health

    Section II. Big Medical Data: Techniques, Managements, and Applications
    5. Big data analytics techniques for healthcare
    6. Big data analytics in precision medicine
    7. Recent advances in processing, interpreting, and managing biological data for therapeutic intervention of human infectious disease
    8. Big data analytics for health: A comprehensive review of techniques and applications

    Section III. Diagnosis and Treatment: Big Data Analytical Techniques, Datasets, Life Cycles, Managements and Applications for Diagnosis and Treatment

    9. Recent applications of data mining in medical diagnosis and prediction
    10. Big medical data analytics for diagnosis
    11. Big data analytics and radiomics to discover diagnostics on different cancer types
    12. Big medical data, cloud computing and artificial intelligence for improving diagnosis in healthcare

    Section IV. Prediction: Big Data Analytical Techniques, Datasets, Life Cycles, Managements and Applications for Prediction

    13. Use of artificial intelligence for predicting infectious disease
    14. Hospital data analytics system for tracking and predicting obese patients' lifestyle habits
    15. Predictions on diabetic patient datasets using big data analytics and machine learning techniques
    16. Skin cancer prediction using big data analytics and AI techniques

    Section V. Big Medical Fake News Analytics for Preventing Medical Misinformation and Myths
    17. COVID-19 fake news analytics from social media using topic modeling and clustering
    18. Big medical data mining system (BigMed) for the detection and classification of COVID-19 misinformation

    Section VI. Challenges and Future of Big Data in Healthcare

    19. Privacy security risks of big data processing in healthcare
    20. Opportunities and challenges in healthcare with the management of big biomedical data
    21. Future direction for healthcare based on big data analytics

    Section VII. Case Studies of Big Data in Healthcare Arena

    22. Big data in orthopedics: Between hypes and hopes
    23. Predicting onset (type-2) of diabetes from medical records using binary class classification
    24. Screening programs incorporating big data analytics

Product details

  • No. of pages: 354
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: May 19, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323919074
  • eBook ISBN: 9780323985161

About the Editor

Pantea Keikhosrokiani

Pantea Keikhosrokiani is a Senior Lecturer at the School of Computer Sciences, Universiti Sains Malaysia (USM; Penang, Malaysia). She was a teaching fellow at the National Advanced IPv6 Centre of Excellence (Nav6), USM. She has received her PhD in Service System Engineering, Information System, and her master’s degree in information technology from the School of Computer Sciences, USM. She has been graduated with Bachelor of Science in Electrical Engineering Electronics. Her recent book is published in 2019 entitled “Perspectives in the Development of Mobile Medical Information Systems: Life Cycle, Management, Methodological Approach and Application”. Her articles have been published in distinguished edited books and journals by Elsevier, Springer, Taylors and Francis, IGI global, etc. and have been indexed by ISI, Scopus and PubMed. Her areas of interest for research and teaching are Information Systems & Analytics, Health, Medical & Business Analytics, Big Data, Behavioral analytics, Opinion mining, Digital Informatics, and Digital Technopreneurship.

Affiliations and Expertise

Senior Lecturer, School of Computer Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia

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