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Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches

Environmental exposure constantly changes with time and various interactions that can affect health outcomes. Machine learning (ML) or deep learning (DL) algorithms have been used to solve complex problems, such as multiple exposures and their interactions. This study developed predictive models for...

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Autores principales: Lee, Woojoo, Lim, Youn-Hee, Ha, Eunhee, Kim, Yoenjin, Lee, Won Kyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281380/
https://www.ncbi.nlm.nih.gov/pubmed/35834079
http://dx.doi.org/10.1007/s11356-022-21768-9
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author Lee, Woojoo
Lim, Youn-Hee
Ha, Eunhee
Kim, Yoenjin
Lee, Won Kyung
author_facet Lee, Woojoo
Lim, Youn-Hee
Ha, Eunhee
Kim, Yoenjin
Lee, Won Kyung
author_sort Lee, Woojoo
collection PubMed
description Environmental exposure constantly changes with time and various interactions that can affect health outcomes. Machine learning (ML) or deep learning (DL) algorithms have been used to solve complex problems, such as multiple exposures and their interactions. This study developed predictive models for cause-specific mortality using ML and DL algorithms with the daily or hourly measured meteorological and air pollution data. The ML algorithm improved the performance compared to the conventional methods, even though the optimal algorithm depended on the adverse health outcomes. The best algorithms were extreme gradient boosting, ridge, and elastic net, respectively, for non-accidental, cardiovascular, and respiratory mortality with daily measurement; they were superior to the generalized additive model reducing a mean absolute error by 4.7%, 4.9%, and 16.8%, respectively. With hourly measurements, the ML model tended to outperform the conventional models, even though hourly data, instead of daily data, did not enhance the performance in some models. The proposed model allows a better understanding and development of robust predictive models for health outcomes using multiple environmental exposures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-21768-9.
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spelling pubmed-92813802022-07-15 Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches Lee, Woojoo Lim, Youn-Hee Ha, Eunhee Kim, Yoenjin Lee, Won Kyung Environ Sci Pollut Res Int Research Article Environmental exposure constantly changes with time and various interactions that can affect health outcomes. Machine learning (ML) or deep learning (DL) algorithms have been used to solve complex problems, such as multiple exposures and their interactions. This study developed predictive models for cause-specific mortality using ML and DL algorithms with the daily or hourly measured meteorological and air pollution data. The ML algorithm improved the performance compared to the conventional methods, even though the optimal algorithm depended on the adverse health outcomes. The best algorithms were extreme gradient boosting, ridge, and elastic net, respectively, for non-accidental, cardiovascular, and respiratory mortality with daily measurement; they were superior to the generalized additive model reducing a mean absolute error by 4.7%, 4.9%, and 16.8%, respectively. With hourly measurements, the ML model tended to outperform the conventional models, even though hourly data, instead of daily data, did not enhance the performance in some models. The proposed model allows a better understanding and development of robust predictive models for health outcomes using multiple environmental exposures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-21768-9. Springer Berlin Heidelberg 2022-07-14 2022 /pmc/articles/PMC9281380/ /pubmed/35834079 http://dx.doi.org/10.1007/s11356-022-21768-9 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, corrected publication 2022Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research Article
Lee, Woojoo
Lim, Youn-Hee
Ha, Eunhee
Kim, Yoenjin
Lee, Won Kyung
Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches
title Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches
title_full Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches
title_fullStr Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches
title_full_unstemmed Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches
title_short Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches
title_sort forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281380/
https://www.ncbi.nlm.nih.gov/pubmed/35834079
http://dx.doi.org/10.1007/s11356-022-21768-9
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