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Updated: May 27, 2026

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An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Development and external validation of a machine learning-based Cox model for predicting in-hospital survival in

Donglin Li1, Xinyi He2, Yanlin Zhou3

  • 1Department of Thoracic Surgery, Suining Central Hospital, Suining, 629000, China.

Scientific Reports
|May 25, 2026
PubMed

Insights

Machine learning accurately predicts survival in classical heatstroke (CHS) patients. This prognostic tool aids clinical decisions and intelligent monitoring for critical care.

Area of Science:

  • Critical Care Medicine
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Classical heatstroke (CHS) is a critical medical emergency requiring precise prognostic evaluation.
  • Existing prognostic tools for CHS are limited, highlighting the need for advanced predictive methods.
  • Machine learning (ML) applications for predicting clinical outcomes in CHS are underexplored.

Purpose of the Study:

  • To develop and externally validate a machine learning-based Cox model for predicting survival in hospitalized CHS patients.
  • To identify robust prognostic features for CHS using an innovative ML framework.
  • To create a visual prognostic tool (nomogram) for clinical use.

Main Methods:

  • A retrospective multicenter study involving 538 CHS patients from eight hospitals in western China.
  • Utilized an ML framework with nine algorithms and 54 combinations to identify predictors.
  • Developed a multivariable Cox regression model incorporating significant predictors, visualized as a nomogram.

Main Results:

  • The Lasso + SuperPC ML combination achieved high predictive accuracy (C-indexes: 0.921 training, 0.811 validation).
  • The developed nomogram demonstrated strong performance in predicting 10-, 20-, and 30-day survival across cohorts (AUCs ranging from 0.710 to 0.91).
  • Calibration curves and Kaplan-Meier analyses confirmed the model's accuracy and ability to stratify patient risk effectively.

Conclusions:

  • An accurate ML-integrated prognostic tool for hospitalized CHS patients was successfully developed and validated.
  • This tool supports personalized clinical decision-making and enhances intelligent prognosis monitoring in critical care settings.
  • The study opens new avenues for leveraging ML in critical care prognosis.

Keywords:
Classic heat strokeMachine learningMultivariate Cox regression analysis, nomogramPrognosisSurvival

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