使用自然语言处理对无家可归者进行纵向研究,使用电子健康记录数据进行不规则的观察
Alec B Chapman1,2, Daniel O Scharfstein2, Ann Elizabeth Montgomery3,4
1IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
电子健康记录 (EHR) 和自然语言处理 (NLP) 可以跟踪无家可归者的住房稳定性. 这项研究提供了对弱势群体的长期结果的见解.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生研究 公共卫生研究
- 计算社会科学 计算社会科学
背景情况:
- 电子健康记录 (EHR) 捕捉了健康的关键社会决定因素 (SDoH),包括无家可归.
- 使用自然语言处理 (NLP) 从临床笔记中提取SDoH数据对于研究至关重要.
- 使用EHR数据对无家可归者的纵向研究是具有挑战性的,因为观察时间不规则.
研究的目的:
- 从EHR临床笔记中提取美国退伍军人事务部 (VA) 患者队列的住房状况.
- 开发和应用统计方法来分析无家可归者历史的个人纵向住房结果.
- 证明EHR数据和NLP对通知住房政策和研究的有用性.
主要方法:
- 使用NLP系统从临床笔记中提取住房状况信息.
- 使用反向强度加权来考虑EHR数据中不规则的观察间隔.
- 应用了通用估计方程 (GEE) 来估计计划后进入不稳定住房的每日概率.
主要成果:
- 这项研究成功地在三年内提取了VA患者队列的住房状况数据.
- 使用反向强度加权和GEE进行调整后的分析提供了每日估计的住房不稳定性.
- 该方法为那些有无家可归史的人提供了对长期住房结果的独特见解.
结论:
- 以观察不规则性进行调整的NLP和EHR数据分析,可以有效地研究长期住房结果.
- 这种方法为专注于无家可归和住房援助计划的研究人员和政策制定者提供了有价值的数据.
- 这些发现突出了利用常规收集的EHR数据用于关键公共卫生研究的潜力.
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