Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
Journals
IJERPH
Special Issues
Machine Learning and Modeling in Epidemiology and Health Policy
ijerph-logo
Submit to IJERPH Review for IJERPH Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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 2336

Special Issue Editors

Dr. Hadi Akbarzadeh Khorshidi

E-Mail Website
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
Dr. Marzieh Soltanolkottabi

E-Mail Website
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

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 1645
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop