Articles | Volume 22, issue 9
https://doi.org/10.5194/nhess-22-3015-2022
https://doi.org/10.5194/nhess-22-3015-2022
Research article
 | 
16 Sep 2022
Research article |  | 16 Sep 2022

Machine learning models to predict myocardial infarctions from past climatic and environmental conditions

Lennart Marien, Mahyar Valizadeh, Wolfgang zu Castell, Christine Nam, Diana Rechid, Alexandra Schneider, Christine Meisinger, Jakob Linseisen, Kathrin Wolf, and Laurens M. Bouwer

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-389', Anonymous Referee #1, 03 Mar 2022
    • AC1: 'Reply to RC1', Laurens Bouwer, 10 May 2022
  • RC2: 'Comment on nhess-2021-389', Francesco Sera, 10 Mar 2022
    • AC2: 'Reply on RC2', Laurens Bouwer, 10 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish as is (15 May 2022) by Vitor Silva
ED: Reconsider after major revisions (further review by editor and referees) (20 May 2022) by Vitor Silva
AR by Laurens Bouwer on behalf of the Authors (01 Jul 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (01 Jul 2022) by Vitor Silva
RR by Anonymous Referee #1 (15 Jul 2022)
RR by Francesco Sera (16 Jul 2022)
ED: Publish subject to minor revisions (review by editor) (23 Jul 2022) by Vitor Silva
AR by Laurens Bouwer on behalf of the Authors (12 Aug 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (16 Aug 2022) by Vitor Silva
ED: Publish as is (17 Aug 2022) by Philip Ward (Executive editor)
AR by Laurens Bouwer on behalf of the Authors (17 Aug 2022)  Manuscript 

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Laurens Bouwer on behalf of the Authors (15 Sep 2022)   Author's adjustment   Manuscript
EA: Adjustments approved (15 Sep 2022) by Vitor Silva
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Short summary
Myocardial infarctions (MIs; heart attacks) are influenced by temperature extremes, air pollution, lack of green spaces and ageing population. Here, we apply machine learning (ML) models in order to estimate the influence of various environmental and demographic risk factors. The resulting ML models can accurately reproduce observed annual variability in MI and inter-annual trends. The models allow quantification of the importance of individual factors and can be used to project future risk.
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