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Machine learning approaches to identify thresholds in a heat‐health warning system context.

Masselot, Pierre, Chebana, Fateh ORCID: https://orcid.org/0000-0002-3329-8179, Campagna, Céline, Lavigne, Eric, Ouarda, Taha B. M. J. ORCID: https://orcid.org/0000-0002-0969-063X et Gosselin, Pierre (2021). Machine learning approaches to identify thresholds in a heat‐health warning system context. Journal of the Royal Statistical Society: Series A (Statistics in Society) , vol. 184 , nº 4. p. 1326-1346. DOI: 10.1111/rssa.12745.

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Résumé

During the last two decades, a number of countries or cities established heat-health warning systems in order to alert public health authorities when some heat indicator exceeds a predetermined threshold. Different methods were considered to establish thresholds all over the world, each with its own strengths and weaknesses. The common ground is that current methods are based on exposure-response function estimates that can fail in many situations. The present paper aims at proposing several data-driven methods to establish thresholds using historical data of health issues and environmental indicators. The proposed methods are model-based regression trees (MOB), multivariate adaptive regression splines (MARS), the patient rule-induction method (PRIM) and adaptive index models (AIM). These methods focus on finding relevant splits in the association between indicators and the health outcome but do it in different fashions. A simulation study and a real-world case study hereby compare the discussed methods. Results show that proposed methods are better at predicting adverse days than current thresholds and benchmark methods. The results nonetheless suggest that PRIM is overall the more reliable method with low variability of results according to the scenario or case.

Type de document: Article
Mots-clés libres: adaptive index models (AIMs); multivariate adaptive regression spline (MARS); patient rule-induction method (PRIM); regression trees; threshold; warning system
Centre: Centre Eau Terre Environnement
Date de dépôt: 15 oct. 2021 17:35
Dernière modification: 15 févr. 2022 20:35
URI: https://espace.inrs.ca/id/eprint/12028

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