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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


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Guest Editor
Melbourne School of Population and Global Health, The University of Melbourne, Melbourne 3052, Australia
Interests: system modeling; health informatics; machine learning and health policy
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Mechanical and Industrial Engineering Department, University of New Haven, West Haven, CT 06516, USA
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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Published Papers (1 paper)

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Research

17 pages, 10065 KiB  
Article
A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting
by Yulan Li and Kun Ma
Int. J. Environ. Res. Public Health 2022, 19(19), 12528; https://doi.org/10.3390/ijerph191912528 - 30 Sep 2022
Viewed by 1748
Abstract
The coronavirus disease 2019 (COVID-19) has spread rapidly around the world since 2020, with a significant fatality rate. Until recently, numerous countries were unable to adequately control the pandemic. As a result, COVID-19 trend prediction has become a hot topic in academic circles. [...] Read more.
The coronavirus disease 2019 (COVID-19) has spread rapidly around the world since 2020, with a significant fatality rate. Until recently, numerous countries were unable to adequately control the pandemic. As a result, COVID-19 trend prediction has become a hot topic in academic circles. Both traditional models and existing deep learning (DL) models have the problem of low prediction accuracy. In this paper, we propose a hybrid model based on an improved Transformer and graph convolution network (GCN) for COVID-19 forecasting. The salient feature of the model in this paper is that rich temporal sequence information is extracted by the multi-head attention mechanism, and then the correlation of temporal sequence information is further aggregated by GCN. In addition, to solve the problem of the high time complexity of the existing Transformer, we use the cosine function to replace the softmax calculation, so that the calculation of query, key and value can be split, and the time complexity is reduced from the original O(N2) to O(N). We only concentrated on three states in the United States, one of which was the most affected, one of which was the least affected, and one intermediate state, in order to make our predictions more meaningful. We use mean absolute percentage error and mean absolute error as evaluation indexes. The experimental results show that the proposed time series model has a better predictive performance than the current DL models and traditional models. Additionally, our model’s convergence outperforms that of the current DL models, offering a more precise benchmark for the control of epidemics. Full article
(This article belongs to the Special Issue Machine Learning and Modeling in Epidemiology and Health Policy)
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