Prediction of clinical efficacy of acupuncture intervention on upper limb dysfunction after ischemic stroke based on machine learning: a study driven by DSA diagnostic reports data.

ABSTRACT:

Objective

To develop a machine learning-based model for predicting the clinical efficacy of acupuncture intervention in patients with upper limb dysfunction following ischemic stroke, and to assess its potential role in guiding clinical practice.

Methods

Data from 1,375 ischemic stroke patients with upper limb dysfunction were collected from two hospitals, including medical records and Digital Subtraction Angiography (DSA) reports. All patients received standardized acupuncture treatment. After screening, 616 datasets were selected for analysis. A prediction model was developed using the AutoGluon framework, with three outcome measures as endpoints: the National Institutes of Health Stroke Scale (NIHSS), Fugl-Meyer Assessment for Upper Extremity (FMA-UE), and the Modified Ba

SUBMITTER: Liu Y 

PROVIDER: S-EPMC11747147 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature