TY  - JOUR
PY  - 2021//
TI  - Machine learning-based mortality prediction model for heat-related illness
JO  - Scientific reports
A1  - Hirano, Yohei
A1  - Kondo, Yutaka
A1  - Hifumi, Toru
A1  - Yokobori, Shoji
A1  - Kanda, Jun
A1  - Shimazaki, Junya
A1  - Hayashida, Kei
A1  - Moriya, Takashi
A1  - Yagi, Masaharu
A1  - Takauji, Shuhei
A1  - Yamaguchi, Junko
A1  - Okada, Yohei
A1  - Okano, Yuichi
A1  - Kaneko, Hitoshi
A1  - Kobayashi, Tatsuho
A1  - Fujita, Motoki
A1  - Yokota, Hiroyuki
A1  - Okamoto, Ken
A1  - Tanaka, Hiroshi
A1  - Yaguchi, Arino
SP  - e9501
EP  - e9501
VL  - 11
IS  - 1
N2  - 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>
LA  - en
SN  - 2045-2322
UR  - http://dx.doi.org/10.1038/s41598-021-88581-1
ID  - ref1
ER  -