Automated-detection of risky alcohol use prior to surgery using natural language processing.

Autor: Vydiswaran VGV; Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.; School of Information, University of Michigan, Ann Arbor, Michigan, USA., Strayhorn A; Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA., Weber K; Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA., Stevens H; Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA., Mellinger J; Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.; Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA., Winder GS; Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.; Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA.; Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA., Fernandez AC; Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.
Jazyk: angličtina
Zdroj: Alcohol, clinical & experimental research [Alcohol Clin Exp Res (Hoboken)] 2024 Jan; Vol. 48 (1), pp. 153-163. Date of Electronic Publication: 2024 Jan 08.
DOI: 10.1111/acer.15222
Abstrakt: 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.
(© 2023 The Authors. Alcohol: Clinical and Experimental Research published by Wiley Periodicals LLC on behalf of Research Society on Alcohol.)
Databáze: MEDLINE
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