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Updated: Apr 17, 2026

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Predicting post-stroke functional outcome using explainable machine learning and integrated data.

Jesper Olsson1, Tara M Stanne2,3, Björn Andersson4

  • 1Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Box 440, 405 30, Gothenburg, Sweden. jesper.olsson@gu.se.

Scientific Reports
|April 15, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict functional outcomes after acute ischemic stroke (AIS). Biomarkers like brain-derived tau (BD-tau) and inflammation proteins offer valuable insights beyond stroke severity for personalized prognostication.

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Area of Science:

  • Neurology
  • Biomarkers
  • Machine Learning

Background:

  • Functional outcomes after acute ischemic stroke (AIS) vary significantly.
  • Current prognostic scores may not fully capture complex patient factors.

Purpose of the Study:

  • To predict 3-month functional outcome after AIS using machine learning.
  • To identify key clinical characteristics and blood biomarkers influencing stroke prognosis.
  • To leverage explainable AI for understanding model performance drivers.

Main Methods:

  • Trained machine learning models (XGBoost, MLP, L1/L2 logistic regression) on data from 506 AIS patients.
  • Utilized Shapley additive global explanations for feature importance assessment.
  • Compared model performance using AUROC and AUPRC metrics.

Main Results:

  • All models demonstrated high predictive accuracy (AUROC 0.900-0.906).
  • The MLP model showed superior precision-recall performance and sensitivity.
  • Stroke severity (NIHSS) was the primary predictor, with BD-tau and inflammation markers providing additional prognostic information.

Conclusions:

  • Machine learning accurately predicts functional outcomes post-AIS.
  • Blood biomarkers, including BD-tau and inflammation proteins, enhance prognostic capabilities beyond clinical stroke severity.
  • These findings support the potential for integrating biomarkers into individualized AIS prognostication strategies.