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Machine learning-based mortality prediction model for heat-related illness

In this study, we aimed to develop and validate a machine learning-based mortality prediction model for hospitalized heat-related illness patients. After 2393 hospitalized patients were extracted from a multicentered heat-related illness registry in Japan, subjects were divided into the training set...

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Autores principales: Hirano, Yohei, Kondo, Yutaka, Hifumi, Toru, Yokobori, Shoji, Kanda, Jun, Shimazaki, Junya, Hayashida, Kei, Moriya, Takashi, Yagi, Masaharu, Takauji, Shuhei, Yamaguchi, Junko, Okada, Yohei, Okano, Yuichi, Kaneko, Hitoshi, Kobayashi, Tatsuho, Fujita, Motoki, Yokota, Hiroyuki, Okamoto, Ken, Tanaka, Hiroshi, Yaguchi, Arino
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8096946/
https://www.ncbi.nlm.nih.gov/pubmed/33947902
http://dx.doi.org/10.1038/s41598-021-88581-1
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author Hirano, Yohei
Kondo, Yutaka
Hifumi, Toru
Yokobori, Shoji
Kanda, Jun
Shimazaki, Junya
Hayashida, Kei
Moriya, Takashi
Yagi, Masaharu
Takauji, Shuhei
Yamaguchi, Junko
Okada, Yohei
Okano, Yuichi
Kaneko, Hitoshi
Kobayashi, Tatsuho
Fujita, Motoki
Yokota, Hiroyuki
Okamoto, Ken
Tanaka, Hiroshi
Yaguchi, Arino
author_facet Hirano, Yohei
Kondo, Yutaka
Hifumi, Toru
Yokobori, Shoji
Kanda, Jun
Shimazaki, Junya
Hayashida, Kei
Moriya, Takashi
Yagi, Masaharu
Takauji, Shuhei
Yamaguchi, Junko
Okada, Yohei
Okano, Yuichi
Kaneko, Hitoshi
Kobayashi, Tatsuho
Fujita, Motoki
Yokota, Hiroyuki
Okamoto, Ken
Tanaka, Hiroshi
Yaguchi, Arino
author_sort Hirano, Yohei
collection PubMed
description In this study, we aimed to develop and validate a machine learning-based mortality prediction model for hospitalized heat-related illness patients. After 2393 hospitalized patients were extracted from a multicentered heat-related illness registry in Japan, subjects were divided into the training set for development (n = 1516, data from 2014, 2017–2019) and the test set (n = 877, data from 2020) for validation. Twenty-four variables including characteristics of patients, vital signs, and laboratory test data at hospital arrival were trained as predictor features for machine learning. The outcome was death during hospital stay. In validation, the developed machine learning models (logistic regression, support vector machine, random forest, XGBoost) demonstrated favorable performance for outcome prediction with significantly increased values of the area under the precision-recall curve (AUPR) of 0.415 [95% confidence interval (CI) 0.336–0.494], 0.395 [CI 0.318–0.472], 0.426 [CI 0.346–0.506], and 0.528 [CI 0.442–0.614], respectively, compared to that of the conventional acute physiology and chronic health evaluation (APACHE)-II score of 0.287 [CI 0.222–0.351] as a reference standard. The area under the receiver operating characteristic curve (AUROC) values were also high over 0.92 in all models, although there were no statistical differences compared to APACHE-II. This is the first demonstration of the potential of machine learning-based mortality prediction models for heat-related illnesses.
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spelling pubmed-80969462021-05-05 Machine learning-based mortality prediction model for heat-related illness Hirano, Yohei Kondo, Yutaka Hifumi, Toru Yokobori, Shoji Kanda, Jun Shimazaki, Junya Hayashida, Kei Moriya, Takashi Yagi, Masaharu Takauji, Shuhei Yamaguchi, Junko Okada, Yohei Okano, Yuichi Kaneko, Hitoshi Kobayashi, Tatsuho Fujita, Motoki Yokota, Hiroyuki Okamoto, Ken Tanaka, Hiroshi Yaguchi, Arino Sci Rep Article In this study, we aimed to develop and validate a machine learning-based mortality prediction model for hospitalized heat-related illness patients. After 2393 hospitalized patients were extracted from a multicentered heat-related illness registry in Japan, subjects were divided into the training set for development (n = 1516, data from 2014, 2017–2019) and the test set (n = 877, data from 2020) for validation. Twenty-four variables including characteristics of patients, vital signs, and laboratory test data at hospital arrival were trained as predictor features for machine learning. The outcome was death during hospital stay. In validation, the developed machine learning models (logistic regression, support vector machine, random forest, XGBoost) demonstrated favorable performance for outcome prediction with significantly increased values of the area under the precision-recall curve (AUPR) of 0.415 [95% confidence interval (CI) 0.336–0.494], 0.395 [CI 0.318–0.472], 0.426 [CI 0.346–0.506], and 0.528 [CI 0.442–0.614], respectively, compared to that of the conventional acute physiology and chronic health evaluation (APACHE)-II score of 0.287 [CI 0.222–0.351] as a reference standard. The area under the receiver operating characteristic curve (AUROC) values were also high over 0.92 in all models, although there were no statistical differences compared to APACHE-II. This is the first demonstration of the potential of machine learning-based mortality prediction models for heat-related illnesses. Nature Publishing Group UK 2021-05-04 /pmc/articles/PMC8096946/ /pubmed/33947902 http://dx.doi.org/10.1038/s41598-021-88581-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Hirano, Yohei
Kondo, Yutaka
Hifumi, Toru
Yokobori, Shoji
Kanda, Jun
Shimazaki, Junya
Hayashida, Kei
Moriya, Takashi
Yagi, Masaharu
Takauji, Shuhei
Yamaguchi, Junko
Okada, Yohei
Okano, Yuichi
Kaneko, Hitoshi
Kobayashi, Tatsuho
Fujita, Motoki
Yokota, Hiroyuki
Okamoto, Ken
Tanaka, Hiroshi
Yaguchi, Arino
Machine learning-based mortality prediction model for heat-related illness
title Machine learning-based mortality prediction model for heat-related illness
title_full Machine learning-based mortality prediction model for heat-related illness
title_fullStr Machine learning-based mortality prediction model for heat-related illness
title_full_unstemmed Machine learning-based mortality prediction model for heat-related illness
title_short Machine learning-based mortality prediction model for heat-related illness
title_sort machine learning-based mortality prediction model for heat-related illness
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8096946/
https://www.ncbi.nlm.nih.gov/pubmed/33947902
http://dx.doi.org/10.1038/s41598-021-88581-1
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