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Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms.

Xinping Lin1,2, Shiteng Lin1,2, XiaoLi Cui3

  • 1School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.

Frontiers in Neurology
|January 10, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict post-stroke disability in mild stroke patients. The developed DAMS tool aids neurologists in making critical decisions for patients at high risk of disability.

Keywords:
decision support toolmachine learningmild strokepost-stroke disabilitypredictive model

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Area of Science:

  • Neurology
  • Medical Informatics
  • Machine Learning

Background:

  • Treatment decisions for mild stroke patients are challenging.
  • Identifying patients at high risk of post-stroke disability (PSD) is crucial.

Purpose of the Study:

  • To develop a machine learning (ML) based decision support tool, DAMS (Disability After Mild Stroke), for mild stroke patients.
  • To identify mild stroke patients at high risk of PSD.
  • To assist neurologists in clinical decision-making in emergency settings.

Main Methods:

  • Prospective data collection of ischemic stroke patients (July 2016-September 2020).
  • Exclusion criteria: thrombolytic therapy, age <18, lack of 3-month mRS, pre-existing disability, NIHSS > 5.
  • Developed and evaluated five ML models using AUC, calibration curves, and decision curve analysis.
  • Utilized SHapley Additive exPlanations (SHAP) for feature importance and constructed rapid-DAMS (R-DAMS).

Main Results:

  • 1,905 mild stroke patients were analyzed; 23.4% experienced PSD (mRS ≥ 2 at 3 months).
  • Support vector machine model was selected for DAMS due to superior calibration and net benefit.
  • NIHSS on admission was identified as the most critical predictive feature by SHAP analysis.
  • R-DAMS showed similar discriminative performance to DAMS but poorer calibration.

Conclusions:

  • DAMS and R-DAMS are prediction-driven decision support tools to aid emergency clinical decisions for mild stroke patients.
  • NIHSS on admission is the strongest predictor of PSD, even with narrow baseline score ranges.