使用电子健康记录和协会规则挖矿识别亲密伴侣暴力的潜伏模式
Azade Tabaie1,2, Shelby A Wyand3, Fan Cao2
1Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute, Washington, DC.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
在急诊室中发现亲密伴侣暴力 (IPV) 是很困难的. 电子健康记录的协会规则挖掘揭示了IPV幸存者的独特模式,有助于改进检测和临床决策.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 数据挖掘 数据挖掘
背景情况:
- 亲密伴侣暴力 (IPV) 是一个主要的公共卫生问题.
- 在急诊部 (ED) 识别IPV是具有挑战性的,因为IPV相关的ICD-10代码的使用不一致.
- 电子健康记录 (EHR) 为IPV检测提供了一个潜在的数据源.
研究的目的:
- 在电子病历数据上使用关联规则挖掘,以识别ED患者女性中隐藏的IPV暗示模式.
- 为了区分确认IPV幸存者和非IPV患者之间的模式.
- 探索以信息学为导向的方法,以提高IPV在临床环境中的检测.
主要方法:
- 将阿普里奥里算法应用于EHR数据.
- 在已确认的IPV幸存者和非IPV患者中分析了同时发生的健康状况之间的关联.
- 使用数据挖掘技术来发现隐藏的模式.
主要成果:
- 在IPV幸存者和非IPV患者之间确定了不同的模式.
- IPV幸存者在身体伤害,心理健康状况 (抑郁症,自杀念头) 和社会经济压力因素之间表现出更强烈的关联.
- 非IPV病例主要与焦虑症和家庭相关的压力因素有关.
结论:
- 使用EHR数据的信息驱动方法可以通过揭示微妙的临床信号来改善IPV检测.
- 该研究强调了数据驱动工具的潜力,以加强IPV查和ED的临床决策.
- 了解这些模式可以为IPV幸存者提供更有效的干预措施.
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