Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and unstructured EHR data

CPs integrating structured and unstructured EHR data enable accurate, reproducible AUD identification, surpassing traditional AUD-specific ICD-based methods. This approach facilitates efficient cohort construction for clinical research, public health surveillance, and quality improvement initiatives targeting AUD and its consequences, addressing a critical gap in identifying patients who may benefit from screening and intervention.

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Publication Details

Journal
Alcohol and Alcoholism
Published
2025-11-16
DOI
https://doi.org/10.1093/alcalc/agaf086
Primary Topic
Alcohol Consumption and Health Effects
Type
article
Field-Weighted Citation Impact
0.00
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article

Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and unstructured EHR data

X. H. Li, Elliot B. Tapper, Xing He, Grant D. Scheiffele et al.
Alcohol and Alcoholism
Alcohol Consumption and Health Effects
article

Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and unstructured EHR data

X. H. Li, Elliot B. Tapper, Xing He, Grant D. Scheiffele, Jingchuan Guo, Lili Zhao, Yao An Lee, Ronald Ramon, Hao Dai, Jiang Bian
article en

Abstract

CPs integrating structured and unstructured EHR data enable accurate, reproducible AUD identification, surpassing traditional AUD-specific ICD-based methods. This approach facilitates efficient cohort construction for clinical research, public health surveillance, and quality improvement initiatives targeting AUD and its consequences, addressing a critical gap in identifying patients who may benefit from screening and intervention.

Alcohol and AlcoholismVol. 61(1)
Northwestern University (US), Indiana University Health (US), Florida Museum of Natural History (US), Regenstrief Institute (US), Purdue University West Lafayette (US), University of Michigan (US), University of Florida (US), Michigan Medicine (US), Indiana University – Purdue University Indianapolis (US), Florida College (US)
Openalex Percentile: Top 28%
Alcohol Consumption and Health Effects
DOIOpen Access PDFOpenAlex
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Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and unstructured EHR data — X. H. Li, Elliot B. Tapper, et al. · Alcohol and Alcoholism (2025) | TGRS Research Map | TGRS