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Interpretable machine learning-based predictive model for malnutrition in subacute post-stroke patients: an internal

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Summary

This study developed a machine learning model to predict malnutrition risk in stroke patients during rehabilitation. The CatBoost (CAT) model accurately identifies patients needing nutritional support, improving care.

Keywords:
CATmachine learningmulticenter studypredictive modelrisk factorssubacute stroke

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Nutritional Science

Background:

  • Malnutrition is prevalent in stroke patients during subacute rehabilitation, increasing mortality and adverse outcomes.
  • Existing tools for predicting malnutrition risk in this population are limited.
  • Early identification of malnutrition risk is crucial for effective intervention.

Purpose of the Study:

  • To develop and validate an interpretable machine learning (ML) model for predicting malnutrition risk in stroke patients undergoing subacute rehabilitation.
  • To create a clinically actionable tool for early malnutrition risk stratification.
  • To improve patient outcomes through timely nutritional interventions.

Main Methods:

  • A multicenter study involving development (n=802) and external validation (n=345) cohorts.
  • Feature selection using LASSO regression and the Boruta algorithm.
  • Training and evaluation of eight ML models, including CatBoost (CAT), using cross-validation and metrics like AUC, calibration curves, and DCA.
  • Interpretability assessed using SHAP analysis.

Main Results:

  • The CAT algorithm demonstrated superior performance, with AUCs of 0.848 (development) and 0.772 (external validation).
  • The model showed good calibration and clinical utility via DCA.
  • SHAP analysis identified age, handgrip strength, and Barthel Index (BI) score as key predictors of malnutrition.

Conclusions:

  • An interpretable ML model (CAT-based) was successfully developed and validated for malnutrition risk screening in subacute stroke patients.
  • The model provides a clinically actionable tool for early risk stratification.
  • This facilitates targeted nutritional interventions and personalized rehabilitation, potentially enhancing patient outcomes.