通过数据驱动的方法在初级保健人口中识别临床上有意义的,重叠的阻塞性呼吸道疾病亚型
Maria Pikoula1, Jennifer K Quint2, Constantinos Kallis2
1Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK. m.pikoula@ucl.ac.uk.
机器学习在一大群患者中确定了七种不同的阻塞性呼吸道疾病亚型,揭示了传统诊断之外的复杂表型. 这些数据驱动的集群为疾病异质性和潜在的向治疗提供了新的见解.
科学领域:
- 肺部医学 肺部医学
- 计算生物学 计算生物学
- 遗传学 是一个遗传学.
背景情况:
- 阻塞性呼吸道疾病,如喘,支气管切开症和COPD是异质的,有重叠的途径和表型.
- 当前的诊断标签可能不能完全捕捉疾病的复杂性,限制治疗的有效性.
- 需要基于数据的方法来确定具有临床意义的亚型.
研究的目的:
- 识别,验证和描述具有临床意义的阻塞性气道疾病亚型.
- 利用电子医疗记录 (EHR) 和无监督的机器学习来发现亚型.
- 探索已识别的亚型与临床结果之间的关联.
主要方法:
- 应用K-means集群对626,651名被诊断患有喘,支气管切开症或COPD的患者的电子病历数据.
- 对21种临床特征的分析,包括风险因素和并发症,并减少了维度.
- 通过GWAS评估集群成员与恶化,死亡率和遗传因素之间的关联.
主要成果:
- 确定了七个不同的患者群体,超越了传统的诊断界限.
- 集群包括诸如"异性亚托皮"和"中性吸烟者"等不同临床特征的个人资料.
- 患有心血管并发症的群体表现出最高的恶化率; 群体6 (中性吸烟者) 显示出较差的结果.
结论:
- 在EHR数据上的无监督机器学习揭示了不同的,部分稳定的阻塞性气道疾病亚型.
- 这些数据驱动的亚型突显了呼吸道疾病表型的复杂性和重叠.
- 这些发现支持聚类方法,以发现临床和生物学上有意义的子组和未来的研究.
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