主题演变在新的落者中发生落事件之前,通过通用医生临床笔记的自然语言处理
Noman Dormosh1,2, Ameen Abu-Hanna1,2, Iacer Calixto1,3
1Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands.
Age and ageing
|February 16, 2024
概括
电子健康记录的动态主题建模揭示了药物,脏护理和在老年人中跌倒之前的医院转诊的趋势日益增加. 早期识别这些不断变化的风险因素是预防跌倒的关键.
科学领域:
- 老年学是指老年学的学科.
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 大多数跌倒风险研究是横截面的,未能捕捉动态的,时间变化的风险因素.
- 电子健康记录 (EHR) 提供了对分析不断变化的跌倒风险轨迹至关重要的纵向数据.
- 自然语言处理 (NLP) 技术,如动态主题建模 (DTM),可以提取这些时间模式.
研究的目的:
- 在临床笔记中识别和跟踪不断变化的跌倒相关主题,用于首次跌倒的个人.
- 为了比较风险因素的时间趋势,在新落地者和非落地者之间.
主要方法:
- 一个用2016-2019年初级保健EHR数据设计的案例和队列研究.
- 案例:在2019年跌倒的个体和之前没有跌倒的人 (2016-18年). 控制组:在整个研究期间,没有跌倒的个体.
- 动态主题建模 (DTM) 应用于临床笔记 (2016-2018);使用斜率分析进行趋势比较.
主要成果:
- 分析了2,384个失败者和2,384个对照者.
- 25个不同的主题显示了失败者和对照者之间的显著趋势差异.
- 诸如药物,脏护理,家庭护理人员,住院/出院和诊断途径转诊等主题在跌倒之前显示出越来越多的趋势.
结论:
- 识别不断变化的健康状况和护理需求对于积极预防跌倒至关重要.
- 解决潜在的动态原因的多因素评估可以减少跌倒和相关伤害.
- 对EHR数据的NLP分析提供了关于时间性跌倒风险因素的宝贵见解.
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