以患者为导向的无监督学习,使用初级保健电子健康记录来发现与中风相关的多病症模式
Marc Delord1, Xiaohui Sun2, Annastazia Learoyd2
1School of Life Course & Population Sciences, Department of Population Health Sciences, King's College London, London, UK. marc.delord@kcl.ac.uk.
BMC primary care
|December 20, 2024
概括
这项研究确定了中风患者长期疾病的八种不同的模式. 这些多病症集群揭示了不同的社会人口结构和风险因素,有助于个性化中风护理.
科学领域:
- 神经学 神经学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 脑卒中是导致长期残疾的主要原因.
- 了解中风后的多病态模式对于有效的患者管理至关重要.
- 现有的研究往往缺乏一种纵向的,以患者为中心的方法来治疗中风相关的疾病.
研究的目的:
- 在中风患者中识别和描述纵向多病态模式.
- 探索这些模式的社会人口和临床异质性.
- 为中风幸存者提供以患者为导向的医疗保健模型的发展提供信息.
主要方法:
- 使用无监督的以患者为导向的集群方法.
- 对30种常见的长期疾病 (LTC) 进行了初级保健电子健康记录 (EHR) 的分析.
- 包括18岁以上的中风患者,来自伦敦南部的普通诊所 (2005-2021年).
主要成果:
- 在9,847名中风患者中确定了8个不同的多发病群.
- 集群显示出不同的社会人口特征 (年龄,性别,种族) 和风险因素.
- 发现了复杂的个人资料,将心理健康,传染病和物质依赖与传统风险因素相结合.
结论:
- 这项研究提供了一种新的纵向,以患者为导向的关于中风相关多病症的观点.
- 调查结果突出了同时出现的疾病和患者个人资料的异质性.
- 为中风幸存者提供更有效和个性化的医疗保健策略的潜力.
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