Machine Learning and Modeling in Epidemiology and Health Policy

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2594

Special Issue Editors


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Guest Editor

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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
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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 IJERPH.

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. Healthcare is an international peer-reviewed open access semimonthly 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 2700 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 (2 papers)

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Research

11 pages, 374 KiB  
Article
Evaluation of the Early Intervention Physiotherapist Framework for Injured Workers in Victoria, Australia: Data Analysis Follow-Up
by Hadi Akbarzadeh Khorshidi, Uwe Aickelin and Andrea de Silva
Healthcare 2023, 11(15), 2205; https://doi.org/10.3390/healthcare11152205 - 04 Aug 2023
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Abstract
Purpose: This study evaluates the performance of the Early Intervention Physiotherapist Framework (EIPF) for injured workers. This study provides a proper follow-up period (3 years) to examine the impacts of the EIPF program on injury outcomes such as return to work (RTW) and [...] Read more.
Purpose: This study evaluates the performance of the Early Intervention Physiotherapist Framework (EIPF) for injured workers. This study provides a proper follow-up period (3 years) to examine the impacts of the EIPF program on injury outcomes such as return to work (RTW) and time to RTW. This study also identifies the factors influencing the outcomes. Methods: The study was conducted on data collected from compensation claims of people who were injured at work in Victoria, Australia. Injured workers who commenced their compensation claims after the first of January 2010 and had their initial physiotherapy consultation after the first of August 2014 are included. To conduct the comparison, we divided the injured workers into two groups: physiotherapy services provided by EIPF-trained physiotherapists (EP) and regular physiotherapists (RP) over the three-year intervention period. We used three different statistical analysis methods to evaluate the performance of the EIPF program. We used descriptive statistics to compare two groups based on physiotherapy services and injury outcomes. We also completed survival analysis using Kaplan–Meier curves in terms of time to RTW. We developed univariate and multivariate regression models to investigate whether the difference in outcomes was achieved after adjusting for significantly associated variables. Results: The results showed that physiotherapists in the EP group, on average, dealt with more claims (over twice as many) than those in the RP group. Time to RTW for the injured workers treated by the EP group was significantly lower than for those who were treated by the RP group, indicated by descriptive, survival, and regression analyses. Earlier intervention by physiotherapists led to earlier RTW. Conclusion: This evaluation showed that the EIPF program achieved successful injury outcomes three years after implementation. Motivating physiotherapists to intervene earlier in the recovery process of injured workers through initial consultation helps to improve injury outcomes. Full article
(This article belongs to the Special Issue Machine Learning and Modeling in Epidemiology and Health Policy)
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17 pages, 889 KiB  
Article
Anomaly Detection in Endemic Disease Surveillance Data Using Machine Learning Techniques
by Peter U. Eze, Nicholas Geard, Ivo Mueller and Iadine Chades
Healthcare 2023, 11(13), 1896; https://doi.org/10.3390/healthcare11131896 - 30 Jun 2023
Cited by 3 | Viewed by 1523
Abstract
Disease surveillance is used to monitor ongoing control activities, detect early outbreaks, and inform intervention priorities and policies. However, data from disease surveillance that could be used to support real-time decisionmaking remain largely underutilised. Using the Brazilian Amazon malaria surveillance dataset as a [...] Read more.
Disease surveillance is used to monitor ongoing control activities, detect early outbreaks, and inform intervention priorities and policies. However, data from disease surveillance that could be used to support real-time decisionmaking remain largely underutilised. Using the Brazilian Amazon malaria surveillance dataset as a case study, in this paper we explore the potential for unsupervised anomaly detection machine learning techniques to discover signals of epidemiological interest. We found that our models were able to provide an early indication of outbreak onset, outbreak peaks, and change points in the proportion of positive malaria cases. Specifically, the sustained rise in malaria in the Brazilian Amazon in 2016 was flagged by several models. We found that no single model detected all anomalies across all health regions. Because of this, we provide the minimum number of machine learning models top-k models) to maximise the number of anomalies detected across different health regions. We discovered that the top three models that maximise the coverage of the number and types of anomalies detected across the thirteen health regions are principal component analysis, stochastic outlier selection, and the minimum covariance determinant. Anomaly detection is a potentially valuable approach to discovering patterns of epidemiological importance when confronted with a large volume of data across space and time. Our exploratory approach can be replicated for other diseases and locations to inform monitoring, timely interventions, and actions towards the goal of controlling endemic disease. Full article
(This article belongs to the Special Issue Machine Learning and Modeling in Epidemiology and Health Policy)
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