在电子健康记录中检测不健康的酒精使用:使用自然语言处理进行的一项比较研究
Xintong Ju1, Jake Solka1, Katherine Weber2
1Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
Drug and alcohol dependence
|October 18, 2025
概括
自然语言处理 (NLP) 与结构化数据 (SD) 相比,在初级保健中显著改善了对不健康酒精使用的识别. NLP捕获了更多的病例,特别是那些严重程度较低的酒精使用障碍 (AUD),补充了传统查.
科学领域:
- 临床信息学 临床信息学
- 公共卫生 公共卫生
- 数字健康数字健康
背景情况:
- 不健康的酒精使用,包括危险的酒精使用和酒精使用障碍 (AUD),往往在初级保健中被低估.
- 结构化数据 (SD) 方法在捕捉不健康酒精使用的全部范围方面存在局限性.
- 自然语言处理 (NLP) 为从临床笔记中识别不健康的酒精使用提供了一个有希望的途径.
研究的目的:
- 预期评估SD和NLP在确定初级保健患者不健康饮酒方面的表现.
- 为了比较SD和NLP方法识别的阳性病例的数量.
主要方法:
- 从一个大型中西部医疗系统 (N=133,144) 提取了电子健康记录 (EHR) 数据.
- 应用了SD (诊断码,酒精查得分) 和NLP的方法来识别病例.
- 招募了170名参与者 (85名由SD,85名由NLP) 进行黄金标准的自我报告措施.
主要成果:
- SD在完整的EHR样本中确定了820个病例,而NLP在完整的EHR样本中确定了48,262个不健康饮酒病例.
- 在被认定为SD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识别的参与者中,20%报告AUD,27.06%报告有风险的酒精使用;SD识别的参与者有更多的AUD症状和心理健康困难.
结论:
- 在初级保健中,NLP大大扩大了对不健康酒精使用的识别,捕捉了SD错过的病例.
- 对于识别患有较轻度酒精使用障碍的患者,NLP特别有效.
- NLP可以作为传统查方法的宝贵补充,用于全面检测不健康的酒精使用.
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Overview
Ethers can be prepared from organic compounds by various methods. Some of them are discussed below,
Preparation of Ethers by Alcohol Dehydration
In this method, in the presence of protic acids, alcohol dehydrates to produce alkenes and ethers under different conditions. For example, in the presence of sulphuric acid, dehydration of ethanol at 413 K yields ethoxyethane, whereas it yields ethene at 443 K.
Ethers can be prepared from organic compounds by various methods. Some of them are discussed below,
Preparation of Ethers by Alcohol Dehydration
In this method, in the presence of protic acids, alcohol dehydrates to produce alkenes and ethers under different conditions. For example, in the presence of sulphuric acid, dehydration of ethanol at 413 K yields ethoxyethane, whereas it yields ethene at 443 K.
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Alcohols possess a higher boiling point than aliphatic hydrocarbons of similar...
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Protection
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