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

Yohei Hirano1, Yutaka Kondo2, Toru Hifumi3

  • 1Department of Emergency and Critical Care Medicine, Juntendo University Urayasu Hospital, Tomioka, 2-1-1, Urayasu, Chiba, 279-0021, Japan. yhirano@juntendo-urayasu.jp.

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|May 5, 2021
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Summary
This summary is machine-generated.

Machine learning models accurately predict mortality in hospitalized heat-related illness patients. These models show improved prediction compared to the APACHE-II score, offering a promising tool for clinical decision-making.

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Area of Science:

  • Medical Informatics
  • Public Health
  • Environmental Medicine

Background:

  • Heat-related illnesses (HRIs) pose a significant public health challenge, leading to substantial hospitalizations and mortality.
  • Accurate prediction of mortality risk in hospitalized HRI patients is crucial for timely and effective clinical management.
  • Existing prediction scores may not fully capture the complexity of HRI outcomes.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting mortality in patients hospitalized with heat-related illnesses.
  • To compare the performance of ML models against the conventional Acute Physiology and Chronic Health Evaluation (APACHE)-II score.

Main Methods:

  • A multicentered registry of 2393 hospitalized HRI patients in Japan was utilized.
  • Data from 2014 and 2017-2019 (n=1516) were used for model development, and data from 2020 (n=877) for validation.
  • Twenty-four variables, including patient characteristics, vital signs, and laboratory data, were used as predictors for ML models (logistic regression, SVM, random forest, XGBoost).

Main Results:

  • ML models demonstrated favorable performance in predicting in-hospital death.
  • The Area Under the Precision-Recall Curve (AUPR) for XGBoost was 0.528, significantly outperforming the APACHE-II score's AUPR of 0.287.
  • All ML models achieved high Area Under the Receiver Operating Characteristic (AUROC) values (over 0.92).

Conclusions:

  • Machine learning models show significant potential for predicting mortality in hospitalized heat-related illness patients.
  • These ML models offer a more effective tool for risk stratification compared to the APACHE-II score.
  • This study represents the first demonstration of ML-based mortality prediction for heat-related illnesses.