Interpretable machine learning for prediction of clinical outcomes in acute ischemic stroke.

ABSTRACT:

Background and aims

Predicting the prognosis of acute ischemic stroke (AIS) is crucial in a clinical setting for establishing suitable treatment plans. This study aimed to develop and validate a machine learning (ML) model that predicts the functional outcome of AIS patients and provides interpretable insights.

Methods

We included AIS patients from a multicenter stroke registry in this prognostic study. ML-based methods were utilized to predict 3-month functional outcomes, which were categorized as either favorable [modified Rankin Scale (mRS) ≤ 2] or unfavorable (mRS ≥ 3). The SHapley Additive exPlanations (SHAP) method was employed to identify significant features and interpret their contributions to the predictions of the model.

Results

The dataset comprised a deri

SUBMITTER: Lee J 

PROVIDER: S-EPMC10513028 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature