Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and unstructured EHR data.
Background Alcohol Use Disorder (AUD) drives significant morbidity through alcohol-related liver disease. Accurate AUD identification in electronic health records is critical for research and care delivery, yet International Classification of Diseases (ICD) code-based algorithms miss many cases whil...
| Publicado en: | Alcohol & Alcoholism Vol. 61; no. 1; pp. 1 - 10 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190916540&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190916540 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07350414 FA3 jtl: Alcohol & Alcoholism issn: 07350414 maglogo: N pubinfo: dt: Jan2026 vid: 61 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 190916540 190916540 190916540 10.1093/alcalc/agaf086 190916540 ppf: 1 ppct: 9 formats: tig: atl: Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and unstructured EHR data. aug: au: Dai, Hao Tapper, Elliot B Zhao, Lili Scheiffele, Grant D Li, Xiaohan Ramon, Ronald He, Xing Lee, Yao An Guo, Jingchuan Bian, Jiang affil: Department of Biostatistics & Health Data Science, Indiana University School of Medicine, 410 W 10th St, Indianapolis, IN 46202, United States sug: subj: Alcoholism Diagnosis Persons with Alcoholism Psychosocial Factors Patient Identification Methods Phenotype Electronic Health Records Natural Language Processing Human Female Male Adult Middle Age Aged Validation Studies Prospective Studies Sensitivity and Specificity Predictive Value of Tests International Classification of Diseases Algorithms Data Mining Medical Informatics Registries, Disease Record Review Random Sample Descriptive Statistics Confidence Intervals Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Background Alcohol Use Disorder (AUD) drives significant morbidity through alcohol-related liver disease. Accurate AUD identification in electronic health records is critical for research and care delivery, yet International Classification of Diseases (ICD) code-based algorithms miss many cases while manual review is impractical at scale. Computable phenotypes (CPs) integrating structured and unstructured EHR data offer a scalable solution. Methods Using University of Florida Health's Integrated Data Repository covering two million patients, we developed AUD CPs through a two-step process. First, candidate cohorts were identified using AUD-related ICD codes, medications, and keyword searches across structured and unstructured data. Second, rule-based combinations were iteratively refined through manual chart review. Final algorithms were evaluated against gold-standard chart review, measuring sensitivity, positive predictive value (PPV), and F1-score, then validated in an independent testing set and an external dataset. Results The F1-optimized CP achieved an F1-score of.87 (sensitivity:.98, PPV:.78) in the testing set, while the precision-optimized CP achieved PPV of.9 (sensitivity:.68, F1-score:.77). Minimal performance attenuation between training and testing sets demonstrated robustness and generalizability. Both CPs substantially outperformed restricted AUD-specific ICD code-based approaches. Conclusions 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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