Interpretable machine learning for predicting early neurological deterioration in symptomatic intracranial atherosclerotic stenosis

This study demonstrates the potential of ML in predicting END in SICAS patients. The SHAP method enhances the interpretability of the prediction model, providing a practical and implementable solution for the early identification of high-risk patients.

Authors

Chunhao Mei
Qingguang Wang
Xiaoning Guo (ORCID: https://orcid.org/0000-0001-7626-1539)
Yang Yang (ORCID: https://orcid.org/0000-0002-1163-2359)
Jiajia Chen (ORCID: https://orcid.org/0000-0002-2068-509X)
Tingting Tao (ORCID: https://orcid.org/0000-0003-2449-9291)

Institutions

Xuzhou Medical College (CN)
Jiangyin People's Hospital (CN)
Jiangyin Traffic Planning Survey & Design Institute (China) (CN)

Publication Details

Journal: Frontiers in Neurology
Published: 2025-10-21
DOI: https://doi.org/10.3389/fneur.2025.1667119
Primary Topic: Acute Ischemic Stroke Management
Type: article
Field-Weighted Citation Impact: 0.00

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article

Interpretable machine learning for predicting early neurological deterioration in symptomatic intracranial atherosclerotic stenosis

Chunhao Mei, Qingguang Wang, Xiaoning Guo, Yang Yang et al.

Oct 21, 2025

Frontiers in Neurology

Acute Ischemic Stroke Management

article

Interpretable machine learning for predicting early neurological deterioration in symptomatic intracranial atherosclerotic stenosis

Chunhao Mei, Qingguang Wang, Xiaoning Guo, Yang Yang, Jiajia Chen, Tingting Tao

October 21, 2025article en

Abstract

This study demonstrates the potential of ML in predicting END in SICAS patients. The SHAP method enhances the interpretability of the prediction model, providing a practical and implementable solution for the early identification of high-risk patients.

Frontiers in NeurologyVol. 16

Xuzhou Medical College (CN), Jiangyin People's Hospital (CN), Jiangyin Traffic Planning Survey & Design Institute (China) (CN)

Openalex Percentile: Top 60%

Acute Ischemic Stroke Management

DOI Open Access PDF OpenAlex

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