{"doi":"10.1111/acer.15222","title":"Automated‐detection of risky alcohol use prior to surgery using natural language processing","abstract":"BACKGROUND: Preoperative risky alcohol use is one of the most common surgical risk factors. Accurate and early identification of risky alcohol use could enhance surgical safety. Artificial Intelligence-based approaches, such as natural language processing (NLP), provide an innovative method to identify alcohol-related risks from patients' electronic health records (EHR) before surgery. METHODS: Clinical notes (n = 53,629) from pre-operative patients in a tertiary care facility were analyzed for evidence of risky alcohol use and alcohol use disorder. One hundred of these records were reviewed by experts and labeled for comparison. A rule-based NLP model was built, and we assessed the clinical notes for the entire population. Additionally, we assessed each record for the presence or absence of alcohol-related International Classification of Diseases (ICD) diagnosis codes as an additional comparator. RESULTS: NLP correctly identified 87% of the human-labeled patients classified with risky alcohol use. In contrast, diagnosis codes alone correctly identified only 29% of these patients. In terms of specificity, NLP correctly identified 84% of the non-risky cohort, while diagnosis codes correctly identified 90% of this cohort. In the analysis of the full dataset, the NLP-based approach identified three times more patients with risky alcohol use than ICD codes. CONCLUSIONS: NLP, an artificial intelligence-based approach, efficiently and accurately identifies alcohol-related risk in patients' EHRs. This approach could supplement other alcohol screening tools to identify patients in need of intervention, treatment, and/or postoperative withdrawal prophylaxis. Alcohol-related ICD diagnosis had limited utility relative to NLP, which extracts richer information within clinical notes to classify patients.","journal":"Alcohol Clinical and Experimental Research","year":2024,"id":436002,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9224,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1244579,"name":"Asher Strayhorn","orcid":null,"position":1,"is_corresponding":false},{"id":1244580,"name":"Katherine Weber","orcid":null,"position":2,"is_corresponding":false},{"id":1244581,"name":"Haley Stevens","orcid":null,"position":3,"is_corresponding":false},{"id":665196,"name":"Jessica L. Mellinger","orcid":"0000-0001-7364-5035","position":4,"is_corresponding":false},{"id":881019,"name":"Gerald Scott Winder","orcid":"0000-0002-2355-7317","position":5,"is_corresponding":false},{"id":464036,"name":"Anne C. Fernandez","orcid":"0000-0002-9129-7318","position":6,"is_corresponding":false},{"id":510422,"name":"V. G. Vinod Vydiswaran","orcid":"0000-0002-3122-1936","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-19T02:00:12.706156Z","pmid":"38189663","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}