@article{ref1,
title="Machine learning-based mortality prediction model for heat-related illness",
journal="Scientific reports",
year="2021",
author="Hirano, Yohei and Kondo, Yutaka and Hifumi, Toru and Yokobori, Shoji and Kanda, Jun and Shimazaki, Junya and Hayashida, Kei and Moriya, Takashi and Yagi, Masaharu and Takauji, Shuhei and Yamaguchi, Junko and Okada, Yohei and Okano, Yuichi and Kaneko, Hitoshi and Kobayashi, Tatsuho and Fujita, Motoki and Yokota, Hiroyuki and Okamoto, Ken and Tanaka, Hiroshi and Yaguchi, Arino",
volume="11",
number="1",
pages="e9501-e9501",
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.<p /> <p>Language: en</p>",
language="en",
issn="2045-2322",
doi="10.1038/s41598-021-88581-1",
url="http://dx.doi.org/10.1038/s41598-021-88581-1"
}