Machine Learning-Based Model for Prediction of Post-Stroke Cognitive Impairment in Acute Ischemic Stroke: A Cross-Sectional Study.

Journal: Neurology India

PMID: 39690991

Abstract

BACKGROUND AND OBJECTIVE: Early identification of post-stroke cognitive impairment (PSCI) is an important challenge for clinicians. In this study, we aimed to build a machine learning-based prediction model for PSCI and uncover potential risk factors to support clinical decision-making.

Authors

Junqin Zhang

Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, China.
Zhaohong Kong

Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, China.
Songlin Hong

F&E Data Technology (Tianjin) Corporation, Tianjin, China.
Zhentao Zhang

c Beijing Key Laboratory of Thermal Science and Technology , Beijing , China.

Keywords

Aged Cognitive Dysfunction Cross-Sectional Studies Female Humans Ischemic Stroke Logistic Models Machine Learning Male Mental Status and Dementia Tests Middle Aged Risk Factors

External Resources

View on PubMed Access via DOI PubMed (39690991)

Machine Learning-Based Model for Prediction of Post-Stroke Cognitive Impairment in Acute Ischemic Stroke: A Cross-Sectional Study.

Abstract

Authors

Keywords

External Resources

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