使用基于电子病历的无监督机器学习集群检测心血管疾病
Ying Hu1,2, Hai Yan3, Ming Liu2,4
1Department of Cardiology, National Clinical Research Center for Interventional Medicine, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
BMC medical research methodology
|December 20, 2024
概括
使用电子医疗记录的无监督机器学习有效检测心血管疾病 (CVD). 这种方法在改善临床环境中的心血管疾病诊断方面是有前途的.
科学领域:
- 机器学习 机器学习
- 医疗信息学 医疗信息学
- 心血管疾病研究研究
背景情况:
- 电子病历 (EMR) 为机器学习 (ML) 模型在心血管疾病 (CVD) 风险预测中提供了丰富的数据.
- 无监督的ML方法可能会在临床实践中开发用于检测流行心血管疾病的新型模型.
研究的目的:
- 测试假设,使用EMR数据的无监督ML可以开发一种用于检测流行心血管疾病的新模型.
- 评估聚类模型在确定大量患者队列中的心血管疾病病例中的有效性.
主要方法:
- 包括从2014年1月到2022年7月的155,894名患者 (≥18岁).
- 采用K-平均集群 (k=2,4,8) 和贝叶斯定理用于预测准确度估计.
- 使用主要组件分析 (PCA) 来减少维度.
主要成果:
- 聚类模型在训练和测试组中都实现了高预测准确度 (0.85-0.87).
- 2-,4-和8-分类模型显示了可比的预测性能.
- 经过PCA,在缩小尺寸后发现CVD和非CVD病例之间存在显著的分离.
结论:
- 与无监督ML相结合的EMR数据可以创建强大的CVD检测模型.
- 需要进一步的纵向研究来完善该模型的临床应用.
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