整合多个数据源来预测药物滥用患者的所有原因的再入院或死亡率
Tim Gruenloh1, Preeti Gupta2,3, Askar Safipour Afshar2
1Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
PLOS digital health
|September 18, 2025
概括
滥用药物的住院患者面临更高的死亡或再入院风险. 通过整合电子健康记录,社会经济和紧急医疗服务数据,可以有效地识别这些有风险的个人,以便及时进行干预.
科学领域:
- 医疗信息学 医疗信息学
- 在医疗保健中的数据科学.
- 公共卫生 公共卫生
背景情况:
- 入院的药物滥用患者增加了不良结果的风险,包括再入院和死亡率.
- 早期识别高风险个体对于及时干预和改善医疗保健资源优化至关重要.
研究的目的:
- 开发和评估用于识别30天死亡或退院后再入院高风险的药物滥用患者的方法.
- 探索各种数据源的整合,以提高风险预测.
主要方法:
- 利用物质滥用数据共享来进行预测建模.
- 使用结构化电子健康记录 (EHR) 数据,非结构化临床笔记,社会经济数据和紧急医疗服务 (EMS) 数据,比较了各种机器学习算法.
- 开发了一个渐变增强的机器模型,集成结构化的EHR,社会经济和EMS数据.
主要成果:
- 结构化EHR,社会经济和EMS数据相结合的渐变增强机器模型实现了最佳性能 (c-统计值为0.746).
- 这种综合模型的性能优于其他机器学习方法和数据源组合.
- 纳入非结构化临床笔记并没有显著改善预测性能.
结论:
- 跨源数据集成,超越传统的电子健康记录,可以显著提高住院药物滥用患者的风险评估.
- 之前的住院,EMS遭遇和出院处置是不良结果的关键预测因素.
- 需要进一步的研究才能有效地利用非结构化临床数据来预测风险.
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