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Updated: Jun 30, 2026

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Machine learning-based prediction model for long-term mortality after ischemic stroke.

Hee-Soo Kim1, Seung-Bo Lee1, Changi Kim2

  • 1Department of Medical Informatics, Keimyung University School of Medicine, Daegu, South Korea.

Scientific Reports
|June 21, 2026
PubMed
Summary

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This summary is machine-generated.

Machine learning models predict long-term stroke survival more accurately than traditional scores. A Gradient Boosting Cox model identified key factors like age and NIH Stroke Scale score for improved patient outcome predictions.

Area of Science:

  • Neurology
  • Medical Informatics
  • Biostatistics

Background:

  • Stroke is a leading cause of mortality, necessitating accurate long-term survival predictions.
  • Existing prediction methods are often short-term focused and data-intensive.
  • There is a need for convenient, time-aware models for post-stroke mortality prediction.

Purpose of the Study:

  • To develop and validate machine learning (ML) and deep learning (DL) models for predicting long-term mortality in acute stroke patients.
  • To compare the performance of ML/DL models against traditional risk scores.
  • To identify key predictive features for practical application.

Main Methods:

  • Development of various ML and DL models using clinical data from 3,411 patients (developmental cohort).
Keywords:
Long-term survivalMachine learningStroke

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  • External validation using data from 502 patients (secondary cardiovascular center).
  • Comparison of the best-performing model against the PREMISE score.

    • Main Results:

    • The Gradient Boosting Cox Proportional Hazards model achieved the highest performance (C-index 0.785 internal, 0.845 external).
    • The ML model significantly outperformed the conventional PREMISE score in the external dataset (C-index 0.845 vs. 0.783).
    • Key predictors included age, National Institutes of Health Stroke Scale score, and hemoglobin levels.

    Conclusions:

    • A validated ML-based model offers superior accuracy for post-stroke survival prediction compared to existing scores.
    • This model can improve individual patient prognostication and aid in medical resource allocation.
    • The identified key features provide practical insights for clinical management.