电子健康记录中不健康酒精使用检测：使用自然语言处理的比较研究。

IF 3.6 2区医学 Q1 PSYCHIATRY

Drug and alcohol dependence Pub Date : 2025-10-10 DOI:10.1016/j.drugalcdep.2025.112920

Xintong Ju , Jake Solka , Katherine Weber , VG Vinod Vydiswaran , Lewei Allison Lin , Erin E. Bonar , Anne C. Fernandez

{"title":"电子健康记录中不健康酒精使用检测：使用自然语言处理的比较研究。","authors":"Xintong Ju , Jake Solka , Katherine Weber , VG Vinod Vydiswaran , Lewei Allison Lin , Erin E. Bonar , Anne C. Fernandez","doi":"10.1016/j.drugalcdep.2025.112920","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Unhealthy alcohol use, including risky alcohol use and alcohol use disorder (AUD), are under-identified in primary care settings. Natural Language Processing (NLP) is a promising approach that could identify unhealthy alcohol use from clinical notes even when structured data (SD) indicators are lacking. This study prospectively evaluated the performance of SD and NLP in identifying unhealthy alcohol use in primary care patients.</div></div><div><h3>Methods</h3><div>We extracted electronic health record (EHR) data of primary care patients at a large Midwestern Health System (N = 133,144) and applied two identification approaches; an SD approach (i.e., diagnostic codes and alcohol screening scores) and an NLP-based approach. We then recruited N = 170 participants identified by SD (N = 85) or NLP (N = 85) to complete gold-standard self-report measures and compared the number of positive cases identified by each method.</div></div><div><h3>Results</h3><div>In the full EHR sample, SD identified 820 cases of unhealthy alcohol use, and NLP identified 48,262 cases with unhealthy alcohol use. Among participants identified by SD, 41.18 % reported AUD, and 28.82 % reported risky alcohol use. Among those identified by NLP, 20 % reported AUD and 27.06 % reported risky alcohol use. Participants identified by SD had more AUD symptoms and mental health difficulties.</div></div><div><h3>Conclusions</h3><div>NLP identified many primary care patients with indicators of unhealthy alcohol use that SD missed, indicating NLP could substantially expand identification of unhealthy alcohol use in primary care populations, particularly those with lower severity alcohol use disorder. NLP could complement traditional screening methods for comprehensive unhealthy alcohol use detection.</div></div>","PeriodicalId":11322,"journal":{"name":"Drug and alcohol dependence","volume":"277 ","pages":"Article 112920"},"PeriodicalIF":3.6000,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unhealthy alcohol use detection in electronic health records: A comparative study using natural language processing\",\"authors\":\"Xintong Ju , Jake Solka , Katherine Weber , VG Vinod Vydiswaran , Lewei Allison Lin , Erin E. Bonar , Anne C. Fernandez\",\"doi\":\"10.1016/j.drugalcdep.2025.112920\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Unhealthy alcohol use, including risky alcohol use and alcohol use disorder (AUD), are under-identified in primary care settings. Natural Language Processing (NLP) is a promising approach that could identify unhealthy alcohol use from clinical notes even when structured data (SD) indicators are lacking. This study prospectively evaluated the performance of SD and NLP in identifying unhealthy alcohol use in primary care patients.</div></div><div><h3>Methods</h3><div>We extracted electronic health record (EHR) data of primary care patients at a large Midwestern Health System (N = 133,144) and applied two identification approaches; an SD approach (i.e., diagnostic codes and alcohol screening scores) and an NLP-based approach. We then recruited N = 170 participants identified by SD (N = 85) or NLP (N = 85) to complete gold-standard self-report measures and compared the number of positive cases identified by each method.</div></div><div><h3>Results</h3><div>In the full EHR sample, SD identified 820 cases of unhealthy alcohol use, and NLP identified 48,262 cases with unhealthy alcohol use. Among participants identified by SD, 41.18 % reported AUD, and 28.82 % reported risky alcohol use. Among those identified by NLP, 20 % reported AUD and 27.06 % reported risky alcohol use. Participants identified by SD had more AUD symptoms and mental health difficulties.</div></div><div><h3>Conclusions</h3><div>NLP identified many primary care patients with indicators of unhealthy alcohol use that SD missed, indicating NLP could substantially expand identification of unhealthy alcohol use in primary care populations, particularly those with lower severity alcohol use disorder. NLP could complement traditional screening methods for comprehensive unhealthy alcohol use detection.</div></div>\",\"PeriodicalId\":11322,\"journal\":{\"name\":\"Drug and alcohol dependence\",\"volume\":\"277 \",\"pages\":\"Article 112920\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Drug and alcohol dependence\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0376871625003734\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug and alcohol dependence","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0376871625003734","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}

引用次数: 0

摘要

背景：不健康的酒精使用，包括危险酒精使用和酒精使用障碍（AUD），在初级保健机构中未被充分识别。自然语言处理（NLP）是一种很有前途的方法，即使在缺乏结构化数据（SD）指标的情况下，也可以从临床记录中识别不健康的酒精使用情况。本研究前瞻性地评估了SD和NLP在识别初级保健患者不健康饮酒方面的表现。方法：我们提取了中西部大型卫生系统（N = 133,144）初级保健患者的电子健康记录（EHR）数据，并应用了两种识别方法；SD方法（即诊断代码和酒精筛查分数）和基于nlp的方法。然后，我们招募了N = 170名通过SD （N = 85）或NLP （N = 85）确定的参与者来完成金标准自我报告测量，并比较了每种方法确定的阳性病例数。结果：在整个EHR样本中，SD识别出820例不健康饮酒，NLP识别出48262例不健康饮酒。在SD识别的参与者中，41.18%报告了AUD， 28.82%报告了危险的酒精使用。在NLP鉴定的患者中，20%报告AUD， 27.06%报告危险饮酒。SD识别的参与者有更多的AUD症状和心理健康问题。结论：NLP识别了许多初级保健患者的不健康酒精使用指标，而SD遗漏了这些指标，这表明NLP可以大大扩大初级保健人群中不健康酒精使用的识别，特别是那些严重程度较低的酒精使用障碍患者。NLP可作为传统筛查方法的补充，用于不健康饮酒综合检测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Unhealthy alcohol use detection in electronic health records: A comparative study using natural language processing

Background

Unhealthy alcohol use, including risky alcohol use and alcohol use disorder (AUD), are under-identified in primary care settings. Natural Language Processing (NLP) is a promising approach that could identify unhealthy alcohol use from clinical notes even when structured data (SD) indicators are lacking. This study prospectively evaluated the performance of SD and NLP in identifying unhealthy alcohol use in primary care patients.

Methods

We extracted electronic health record (EHR) data of primary care patients at a large Midwestern Health System (N = 133,144) and applied two identification approaches; an SD approach (i.e., diagnostic codes and alcohol screening scores) and an NLP-based approach. We then recruited N = 170 participants identified by SD (N = 85) or NLP (N = 85) to complete gold-standard self-report measures and compared the number of positive cases identified by each method.

Results

In the full EHR sample, SD identified 820 cases of unhealthy alcohol use, and NLP identified 48,262 cases with unhealthy alcohol use. Among participants identified by SD, 41.18 % reported AUD, and 28.82 % reported risky alcohol use. Among those identified by NLP, 20 % reported AUD and 27.06 % reported risky alcohol use. Participants identified by SD had more AUD symptoms and mental health difficulties.

Conclusions

NLP identified many primary care patients with indicators of unhealthy alcohol use that SD missed, indicating NLP could substantially expand identification of unhealthy alcohol use in primary care populations, particularly those with lower severity alcohol use disorder. NLP could complement traditional screening methods for comprehensive unhealthy alcohol use detection.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Drug and alcohol dependence 医学-精神病学

CiteScore

7.40

自引率

7.10%

发文量

409

审稿时长

41 days

期刊介绍： Drug and Alcohol Dependence is an international journal devoted to publishing original research, scholarly reviews, commentaries, and policy analyses in the area of drug, alcohol and tobacco use and dependence. Articles range from studies of the chemistry of substances of abuse, their actions at molecular and cellular sites, in vitro and in vivo investigations of their biochemical, pharmacological and behavioural actions, laboratory-based and clinical research in humans, substance abuse treatment and prevention research, and studies employing methods from epidemiology, sociology, and economics.