通过将结构化变量标签关系纳入相似度指标,改善患者聚类
Judith Lambert1,2,3, Anne-Louise Leutenegger4, Anaïs Baudot5,6,7
1Sorbonne Université, Université Paris Cité, INSERM, Centre de Recherche des Cordeliers, Paris, F-75006, France. judith.lambert@inserm.fr.
BMC medical research methodology
|March 16, 2025
概括
将结构化标签关系纳入患者相似性测量中可以增强患者分层. 这种方法可以更好地识别相似的患者,并为健康调查提供临床上有意义的见解.
科学领域:
- 医疗信息学 医疗信息学
- 医疗数据分析 医学数据分析
- 计算生物学 计算生物学
背景情况:
- 患者分层对于治疗疗效估计和健康研究中的患者匹配至关重要.
- 医疗记录中的临床变量,带有结构化的标签,用于计算患者相似性.
- 变量标签关系对患者相似度指标的影响尚未得到充分研究.
研究的目的:
- 调整和评估包含结构化标签关系的加权Cosine相似度指标.
- 使用这些增强措施,从医疗行政数据库中计算患者的相似性.
主要方法:
- 60岁以上的患者根据法国医疗行政数据库的年度药品退款进行分组.
- 标准Cosine相似度与权重版本的比较,考虑可变频率和标签关系.
- 使用马尔科夫集群算法构建和集群的患者网络,通过诊断丰富测试进行评估.
主要成果:
- 包含结构化标签关系的加权相似度指标在识别相似患者方面表现优异.
- 这些加强的措施导致识别了更多与各种诊断丰富相关的集群.
- 丰富试验给出了对已识别的患者群体的临床解释性见解.
结论:
- 在患者相似性计算中考虑变量标签关系显著改善了基于健康状况的患者分层.
- 这种方法为医学研究和临床应用提供了对患者队列的更细致的理解.
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