无监督识别喘症状亚型支持可治疗的特征方法
Kazuki Hamada1, Takeshi Abe2, Keiji Oishi3
1Department of Systems Bioinformatics, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan; Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan.
概括
一个新的算法使用患者报告的结果来识别喘亚型及其可治疗的特征,即使在资源有限的环境中也能够实现个性化医疗.
科学领域:
- 数字健康数字健康
- 精准医学是一门精准的医学.
- 机器学习在医疗保健中的应用
背景情况:
- 由于喘的异质性,喘管理需要个性化的方法.
- 对喘可治疗特征的客观测量通常无法在资源有限的环境中获得.
- 患者报告结果 (PROs) 为评估喘特征提供了一个可扩展的替代方案.
研究的目的:
- 开发和验证使用无监督机器学习的算法,从PRO数据中估计关键的喘可治疗特征.
- 为了使精确的医学喘在低资源的环境.
主要方法:
- 应用分层聚类和统一多重近似和投影 (UMAP) 到喘控制问卷 (ACQ) -5数据从两个喘队列.
- 在患者症状数据上利用无监督机器学习来识别不同的喘集群.
主要成果:
- 确定了五个基于症状的喘群,每个与特定的可治疗特征相关,如空气流量限制,T2炎症和恶化频率.
- UMAP可视化证实了不同的患者分组,控制不良的喘患者属于特定的集群.
- 结果在独立的队列中进行了外部验证,证明了强度.
结论:
- 一个新的算法成功地将喘患者根据PRO分为五个亚型,揭示了可治疗的特征.
- 这种数据驱动的数字健康方法在资源有限的环境中促进了精确的喘医学.
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