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Big Data, Decision Models, and Public Health

Author

Listed:
  • Chien-Lung Chan

    (Department of Information Management, Yuan Ze University, Taoyuan 320, Taiwan
    Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 320, Taiwan)

  • Chi-Chang Chang

    (School of Medical Informatics, Chung Shan Medical University & IT Office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan)

Abstract

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

Suggested Citation

  • Chien-Lung Chan & Chi-Chang Chang, 2020. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 17(18), pages 1-7, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6723-:d:413983
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    References listed on IDEAS

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    Cited by:

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    3. Daniele Piovani & Stefanos Bonovas, 2022. "Real World—Big Data Analytics in Healthcare," IJERPH, MDPI, vol. 19(18), pages 1-3, September.
    4. Hao-Yun Kao & Chi-Chang Chang & Chin-Fang Chang & Ying-Chen Chen & Chalong Cheewakriangkrai & Ya-Ling Tu, 2022. "Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease," IJERPH, MDPI, vol. 19(3), pages 1-11, January.

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