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Machine Learning and Public Health

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences & Services".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 9519

Special Issue Editors

1. Independent Scientist, Centre for Addiction and Mental Health, Toronto, ON M5T 2S8, Canada
2. Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 2S8, Canada
3. Collaborative Specialization in Addiction Studies, Toronto, ON M5T 2S8, Canada
Interests: tobacco control; nicotine; public health; harm reduction; mental health; health promotion; addiction research
1. Vector Institute, Toronto, ON M5G 1M1, Canada
2. Institute of Health Policy, Management, and Evaluation (IHPME​), University of Toronto, Toronto, ON M5S 1A1, Canada
Interests: motion tracking; pattern recognition; computer vision; image processing; public health
Dalla Lana School of Public Health, University of Toronto, 155 College Street 6th Floor, Toronto, ON M5T3M7, Canada
Interests: population health; analytics; health system sustainability; equity; prevention; health services research; epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are organizing a Special Issue on the use of Artificial Intelligence and Machine Learning to inform public health research and practice for the International Journal of Environmental Research and Public Health. This journal is peer-reviewed and publishes articles in the interdisciplinary area of environmental health sciences and public health.

Artificial Intelligence methodologies including machine learning have the possibility of transforming public health research and impact. These emerging methodologies offer public health new tools to be able to address problems for which classical methodologies are not well suited. This Special Issue welcomes articles that use artificial intelligence/machine learning to address public health issues, such as:

  • Identifying emerging threats (e.g., COVID-19) more quickly;
  • Providing more detailed and up-to-date understanding of population disease and risk factor distributions (e.g., online disease surveillance tools, targeted lead inspections);
  • Improving forecasting of disease incidence of population health planning;
  • Improving targeting of health promotion activities (e.g., sentiment analysis, online tools/apps);
  • Integrating artificial intelligence and machine learning into causal analysis of public health topics.

We are also interested in articles that address methodological concerns over artificial intelligence and machine learning such as:

  • Explainability;
  • Bias;
  • Potential for increased health inequities;
  • Privacy concerns;
  • Data access and sharing;
  • Outdated data and analytic infrastructure;
  • Lack of AI education and skills within public health.

Dr. Michael Chaiton
Dr. Laura Rosella
Dr. Elham Dolatabadi
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

  • artificial intelligence
  • machine learning
  • public health
  • surveillance
  • causal inference
  • health promotion

Published Papers (5 papers)

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Research

40 pages, 59561 KiB  
Article
Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-Leveraged Transmission Models
by Xiaoyan Li, Vyom Patel, Lujie Duan, Jalen Mikuliak, Jenny Basran and Nathaniel D. Osgood
Int. J. Environ. Res. Public Health 2024, 21(2), 193; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph21020193 - 07 Feb 2024
Viewed by 1092
Abstract
COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with [...] Read more.
COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, and support quantitative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation, and multi-year daily use for public health and clinical support decision-making of a particle-filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian sequential Monte Carlo algorithm of particle filtering allows the model to be regrounded daily and adapt to new trends within daily incoming data—including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts of dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of the model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of the dynamic model state, support a probabilistic model projection of epidemiology and health system capacity utilization and service demand, and probabilistically evaluate tradeoffs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including the support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of the Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision-making across Canada, such methods offer strong support for evidence-based public health decision-making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams. Full article
(This article belongs to the Special Issue Machine Learning and Public Health)
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12 pages, 1373 KiB  
Article
Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table
by Christoffer Dharma, Rui Fu and Michael Chaiton
Int. J. Environ. Res. Public Health 2023, 20(13), 6194; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20136194 - 21 Jun 2023
Cited by 1 | Viewed by 1552
Abstract
There is a lack of rigorous methodological development for descriptive epidemiology, where the goal is to describe and identify the most important associations with an outcome given a large set of potential predictors. This has often led to the Table 2 fallacy, where [...] Read more.
There is a lack of rigorous methodological development for descriptive epidemiology, where the goal is to describe and identify the most important associations with an outcome given a large set of potential predictors. This has often led to the Table 2 fallacy, where one presents the coefficient estimates for all covariates from a single multivariable regression model, which are often uninterpretable in a descriptive analysis. We argue that machine learning (ML) is a potential solution to this problem. We illustrate the power of ML with an example analysis identifying the most important predictors of alcohol abuse among sexual minority youth. The framework we propose for this analysis is as follows: (1) Identify a few ML methods for the analysis, (2) optimize the parameters using the whole data with a nested cross-validation approach, (3) rank the variables using variable importance scores, (4) present partial dependence plots (PDP) to illustrate the association between the important variables and the outcome, (5) and identify the strength of the interaction terms using the PDPs. We discuss the potential strengths and weaknesses of using ML methods for descriptive analysis and future directions for research. R codes to reproduce these analyses are provided, which we invite other researchers to use. Full article
(This article belongs to the Special Issue Machine Learning and Public Health)
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14 pages, 677 KiB  
Article
Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors
by Govinda R. Poudel, Anthony Barnett, Muhammad Akram, Erika Martino, Luke D. Knibbs, Kaarin J. Anstey, Jonathan E. Shaw and Ester Cerin
Int. J. Environ. Res. Public Health 2022, 19(17), 10977; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191710977 - 02 Sep 2022
Cited by 8 | Viewed by 2458
Abstract
The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian [...] Read more.
The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34–97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r2 = 0.43) and memory (r2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r2 = 0.29) but weakly predicted memory (r2 = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data. Full article
(This article belongs to the Special Issue Machine Learning and Public Health)
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22 pages, 2953 KiB  
Article
A Novel Approach on Deep Learning—Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs’ Effect over COVID-19 Pandemic
by Yong-Ju Jang, Min-Seung Kim, Chan-Ho Lee, Ji-Hye Choi, Jeong-Hee Lee, Sun-Hong Lee and Tae-Eung Sung
Int. J. Environ. Res. Public Health 2022, 19(11), 6763; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19116763 - 01 Jun 2022
Cited by 3 | Viewed by 1693
Abstract
Following the outbreak of the COVID-19 pandemic, the continued emergence of major variant viruses has caused enormous damage worldwide by generating social and economic ripple effects, and the importance of PHSMs (Public Health and Social Measures) is being highlighted to cope with this [...] Read more.
Following the outbreak of the COVID-19 pandemic, the continued emergence of major variant viruses has caused enormous damage worldwide by generating social and economic ripple effects, and the importance of PHSMs (Public Health and Social Measures) is being highlighted to cope with this severe situation. Accordingly, there has also been an increase in research related to a decision support system based on simulation approaches used as a basis for PHSMs. However, previous studies showed limitations impeding utilization as a decision support system for policy establishment and implementation, such as the failure to reflect changes in the effectiveness of PHSMs and the restriction to short-term forecasts. Therefore, this study proposes an LSTM-Autoencoder-based decision support system for establishing and implementing PHSMs. To overcome the limitations of existing studies, the proposed decision support system used a methodology for predicting the number of daily confirmed cases over multiple periods based on multiple output strategies and a methodology for rapidly identifying varies in policy effects based on anomaly detection. It was confirmed that the proposed decision support system demonstrated excellent performance compared to models used for time series analysis such as statistical models and deep learning models. In addition, we endeavored to increase the usability of the proposed decision support system by suggesting a transfer learning-based methodology that can efficiently reflect variations in policy effects. Finally, the decision support system proposed in this study provides a methodology that provides multi-period forecasts, identifying variations in policy effects, and efficiently reflects the effects of variation policies. It was intended to provide reasonable and realistic information for the establishment and implementation of PHSMs and, through this, to yield information expected to be highly useful, which had not been provided in the decision support systems presented in previous studies. Full article
(This article belongs to the Special Issue Machine Learning and Public Health)
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20 pages, 20278 KiB  
Article
Unsupervised Learning Applied to the Stratification of Preterm Birth Risk in Brazil with Socioeconomic Data
by Márcio L. B. Lopes, Jr., Raquel de M. Barbosa and Marcelo A. C. Fernandes
Int. J. Environ. Res. Public Health 2022, 19(9), 5596; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19095596 - 05 May 2022
Cited by 1 | Viewed by 1498
Abstract
Preterm birth (PTB) is a phenomenon that brings risks and challenges for the survival of the newborn child. Despite many advances in research, not all the causes of PTB are already clear. It is understood that PTB risk is multi-factorial and can also [...] Read more.
Preterm birth (PTB) is a phenomenon that brings risks and challenges for the survival of the newborn child. Despite many advances in research, not all the causes of PTB are already clear. It is understood that PTB risk is multi-factorial and can also be associated with socioeconomic factors. Thereby, this article seeks to use unsupervised learning techniques to stratify PTB risk in Brazil using only socioeconomic data. Through the use of datasets made publicly available by the Federal Government of Brazil, a new dataset was generated with municipality-level socioeconomic data and a PTB occurrence rate. This dataset was processed using various unsupervised learning techniques, such as k-means, principal component analysis (PCA), and density-based spatial clustering of applications with noise (DBSCAN). After validation, four clusters with high levels of PTB occurrence were discovered, as well as three with low levels. The clusters with high PTB were comprised mostly of municipalities with lower levels of education, worse quality of public services—such as basic sanitation and garbage collection—and a less white population. The regional distribution of the clusters was also observed, with clusters of high PTB located mostly in the North and Northeast regions of Brazil. The results indicate a positive influence of the quality of life and the offer of public services on the reduction in PTB risk. Full article
(This article belongs to the Special Issue Machine Learning and Public Health)
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