数据挖掘以确定心力衰竭的正确患者的正确干预措施:现实世界的研究
Keni Lee1, Ramzi Argoubi2, Halley Costantino3
1Sanofi, Reading, UK.
Healthcare informatics research
|February 20, 2025
概括
机器学习确定了不同的心力衰竭患者原型,揭示了量身定制的干预措施可以改善结果. 这种方法有助于个性化护理,在现实世界心力衰竭管理中获得更好的结果.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
背景情况:
- 心力衰竭 (HF) 管理需要个性化干预.
- 将健康的社会决定因素 (SDOH) 与临床数据相结合,可以完善患者分层.
- 机器学习 (ML) 提供了识别复杂患者个人资料的潜力.
研究的目的:
- 在链接的临床和SDOH数据上利用ML来确定针对性心力衰竭干预的患者原型.
- 确定数字医疗 (DHC) 和非DHC干预措施在已识别的患者原型中的有效性.
主要方法:
- 追溯队列研究将科莫多健康索赔数据与国家健康和健康调查 (NHWS) SDOH数据 (2014-2020) 联系起来.
- 应用K-平均集群 (ML) 来识别患者原型.
- 主要结局:与HF相关的住院治疗;次要结局:所有原因的住院治疗和死亡率.
主要成果:
- 鉴定出了两个不同的HF患者原型.
- 原型1型患者年龄较大,疾病严重程度较大,并发病率较高,HF相关住院病例较高.
- 干预类型 (DHC/非DHC) 并没有改变原型之间的观察结果趋势.
结论:
- 来自综合临床和SDOH数据的患者原型增强了对HF异质性的理解.
- 这种方法有助于确定针对不同患者的具体干预措施.
- 根据已识别的原型来个性化干预对于优化心力衰竭患者的结果至关重要.
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