Comparing Machine Learning and Traditional Models to Predict One‐Year Post‐Stroke Dementia Risk

Our findings demonstrate that both machine learning and traditional statistical approaches may effectively predict one-year post-stroke dementia risk using EHR data, which could help identify high-risk patients for early monitoring.

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Publication Details

Journal
Alzheimer s & Dementia
Published
2025-12-01
DOI
https://doi.org/10.1002/alz70860_106285
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Comparing Machine Learning and Traditional Models to Predict One‐Year Post‐Stroke Dementia Risk

Jiahui Dai, Bruce Albala, Alissa Kurzman, Desiree Gutierrez et al.
Alzheimer s & Dementia
Machine Learning in Healthcare
article

Comparing Machine Learning and Traditional Models to Predict One‐Year Post‐Stroke Dementia Risk

Jiahui Dai, Bruce Albala, Alissa Kurzman, Desiree Gutierrez, Xueting Ding, Liner Xiang, Bernadette Boden‐Albala, Yang Meng, Megan Castro
article en

Abstract

Our findings demonstrate that both machine learning and traditional statistical approaches may effectively predict one-year post-stroke dementia risk using EHR data, which could help identify high-risk patients for early monitoring.

Alzheimer s & DementiaVol. 21(S6)
Samueli Institute (US), Irvine University (US)
Good health and well-being
Openalex Percentile: Top 19%
Machine Learning in Healthcare
DOIOpen Access PDFOpenAlex
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