Machine learning-based mortality prediction model for heat-related illness
Abstract 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 tra...
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Nature Portfolio
2021
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oai:doaj.org-article:a339b3e4021e4bf29780ed3e55dd3c722021-12-02T15:37:58ZMachine learning-based mortality prediction model for heat-related illness10.1038/s41598-021-88581-12045-2322https://doaj.org/article/a339b3e4021e4bf29780ed3e55dd3c722021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88581-1https://doaj.org/toc/2045-2322Abstract 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.Yohei HiranoYutaka KondoToru HifumiShoji YokoboriJun KandaJunya ShimazakiKei HayashidaTakashi MoriyaMasaharu YagiShuhei TakaujiJunko YamaguchiYohei OkadaYuichi OkanoHitoshi KanekoTatsuho KobayashiMotoki FujitaHiroyuki YokotaKen OkamotoHiroshi TanakaArino YaguchiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021) |
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Medicine R Science Q Yohei Hirano Yutaka Kondo Toru Hifumi Shoji Yokobori Jun Kanda Junya Shimazaki Kei Hayashida Takashi Moriya Masaharu Yagi Shuhei Takauji Junko Yamaguchi Yohei Okada Yuichi Okano Hitoshi Kaneko Tatsuho Kobayashi Motoki Fujita Hiroyuki Yokota Ken Okamoto Hiroshi Tanaka Arino Yaguchi Machine learning-based mortality prediction model for heat-related illness |
| description |
Abstract 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. |
| format |
article |
| author |
Yohei Hirano Yutaka Kondo Toru Hifumi Shoji Yokobori Jun Kanda Junya Shimazaki Kei Hayashida Takashi Moriya Masaharu Yagi Shuhei Takauji Junko Yamaguchi Yohei Okada Yuichi Okano Hitoshi Kaneko Tatsuho Kobayashi Motoki Fujita Hiroyuki Yokota Ken Okamoto Hiroshi Tanaka Arino Yaguchi |
| author_facet |
Yohei Hirano Yutaka Kondo Toru Hifumi Shoji Yokobori Jun Kanda Junya Shimazaki Kei Hayashida Takashi Moriya Masaharu Yagi Shuhei Takauji Junko Yamaguchi Yohei Okada Yuichi Okano Hitoshi Kaneko Tatsuho Kobayashi Motoki Fujita Hiroyuki Yokota Ken Okamoto Hiroshi Tanaka Arino Yaguchi |
| author_sort |
Yohei Hirano |
| title |
Machine learning-based mortality prediction model for heat-related illness |
| title_short |
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_sort |
machine learning-based mortality prediction model for heat-related illness |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/a339b3e4021e4bf29780ed3e55dd3c72 |
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