Machine Learning and Modeling in Epidemiology and Health Policy
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Communication and Informatics".
Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2457
Special Issue Editors
Interests: system modeling; health informatics; machine learning and health policy
Special Issues, Collections and Topics in MDPI journals
Interests: pandemic modeling; optimization; game theory and medical decision support
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine Learning (ML) and Operations Research (OR) have been used frequently in recent years in many areas, including healthcare and medicine. Even though they facilitate processing large amounts of information and the evaluation of complex systems, not all of the advantages of ML and OR have been exploited in healthcare, particularly in epidemiology and health policy. Due to the inherent complexity of healthcare systems, advanced ML and modeling techniques should be developed for use in prediction, policy evaluation, and decision making. These advanced techniques include reinforcement learning, active learning, transfer learning, semi-supervised learning, and ensemble learning for ML, and game theory, combinatorial optimization, and dynamic programming for modeling. This Special Issue aims to highlight the importance of ML, OR, and system modeling in epidemiology and health policy. It also aims to initiate developing innovative and hybrid ML and decision-making methods with applications in healthcare decision-making. This Special Issue will attract researchers and practitioners working in digital health, pandemic modeling, health policy, and medical informatics.
You may choose our Joint Special Issue in Healthcare.
Dr. Hadi Akbarzadeh Khorshidi
Dr. Marzieh Soltanolkottabi
Guest Editors
Manuscript Submission Information
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Keywords
- ML and OR in healthcare decision making
- pandemic modeling
- innovative ML techniques for health policy evaluation
- simulation techniques for epidemiology and health policy
- optimal decision-making for complex healthcare systems
- ML and modeling for precision and personalized medicine
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