Machine learning for modelling the health impacts of extreme heat: A comprehensive literature review.
Open accessAbstract
Extreme heat ranks among the deadliest weather events globally. The number of heat-related deaths is expected to rise sharply as population ages and climate changes. In recent years, machine learning (ML) approaches have been increasingly used across a range of environmental and health fields, including heat-health studies. In this paper, we conducted a comprehensive literature review of the current ML applications for modelling the human health impacts of extreme heat. We searched for relevant scientific articles published in English in PubMed, Scopus and Web of Science databases from their inception to the search date of December 20, 2024. After screening titles, abstracts and full-texts, 25 papers were included in this review. We found that most of the studies were conducted in high-income countries such as Japan, Canada, South Korea or the United States. The studies primarily modelled a single health outcome, including all-cause mortality or heat-related illness, in the general population. The main predictors were maximum and mean air temperature, followed by relative humidity, and various temporal and socio-demographic variables. The most commonly used approach was Random Forest. Results were mixed regarding the optimal algorithm and the most important predictors. This review highlighted the strengths and limitations of current ML applications in heat-health studies. We propose recommendations to help guide the future development of these approaches to reduce the heat-re…
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Deep learning methods have received increasing attention in recent years because they have shown good results in spatiotemporal prediction problems.
“For that reason, deep learning methods have received increasing attention in recent years because they have shown good results in spatiotemporal prediction problems [14, 15].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [14]
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- Authors
- Boudreault, J., Lamothe, F., Campagna, C., & Chebana, F.
- Affiliations
- Institut National de la Recherche Scientifique (CA)
- Institut National de Santé Publique du Québec (CA)
- Corresponding author
- Boudreault, Jérémie
- Venue
- Environment International
- Publisher
- Elsevier BV
- Published
- 2025-12
- Volume
- 206
- Pages
- 109965
- ISSN
- 0160-4120 (ISSN-L)
- OpenAlex
- W4416714246
- Cited by
- 10
- References
- 80
- Citations by year
- 2026: 10
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Environment International (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Quebec Research Fund Society and Culture (0CAR-360085)
- Institut national de santé publique du Québec
- NSERC (-180821)
- CIHR (-317725, #IF1-184093)
- Institut national de la recherche scientifique
- Fonds de recherche du Québec
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
- Health Sciences › Medicine › Physiology › Thermoregulation and physiological responses
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
- Keywords
- extreme heat, heat-related mortality, heat-related illness, machine learning, random forest, deep learning, air temperature, relative humidity, sociodemographic predictors, vulnerable populations, data-scarce regions
- MeSH terms
- Machine Learning, Humans, Models, Theoretical, Extreme Heat, Climate Change
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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