Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data.
Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data.
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DOI：
10.1016/j.jaci.2013.11.042
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发表时间：
2014-05
影响因子：
14.2
通讯作者：
Wenzel, Sally E.
中科院分区：
文献类型：
--
作者：
Wu, Wei;Bleecker, Eugene;Moore, Wendy;Busse, William W.;Castro, Mario;Chung, Kian Fan;Calhoun, William J.;Erzurum, Serpil;Gaston, Benjamin;Israel, Elliot;Curran-Everett, Douglas;Wenzel, Sally E.
关键词：
以前的研究已经根据少量的临床、生理或炎症特征确定了哮喘表型。然而，还没有研究使用机器学习方法来使用大范围的变量。利用血液、支气管镜、呼出一氧化氮和来自严重哮喘研究项目的临床数据，使用无监督聚类方法识别哮喘的亚表型，然后使用监督学习方法对其进行表征。无监督聚类方法应用于来自378名受试者的112个临床、生理和炎症变量。采用变量选择和监督学习技术来选择相关和非冗余变量，解决它们的预测值，以及整个变量集的预测值。本研究共发现10个变量聚类和6个主题聚类，这些聚类与以往的聚类存在差异和重叠。传统上定义的严重哮喘患者分布在受试者群3-6。第4组为早发性变应性哮喘患者，伴有肺功能低下和嗜酸性粒细胞炎症。发病较晚，以重度哮喘伴鼻息肉和嗜酸性粒细胞增多为特征。第6组哮喘患者表现为持续的血液和支气管肺泡灌洗炎症，尽管全身大量使用皮质类固醇和副作用，但病情加重。哮喘发病年龄、生活质量、症状、药物和卫生保健利用是区分受试者群的51个非冗余变量中的一些。这51个变量以88%的准确率对测试用例进行分类，而所有112个变量的准确率为93%。这里使用的无监督机器学习方法提供了对疾病的独特见解，证实了其他方法，同时揭示了新的额外表型。
Previous studies have identified asthma phenotypes based on small numbers of clinical, physiologic or inflammatory characteristics. However, no studies have utilized a wide range of variables using machine learning approaches. To identify subphenotypes of asthma utilizing blood, bronchoscopic, exhaled nitric oxide and clinical data from the Severe Asthma Research Program using unsupervised clustering, and then characterize them using supervised learning approaches. Unsupervised clustering approaches were applied to 112 clinical, physiologic and inflammatory variables from 378 subjects. Variable selection and supervised learning techniques were employed to select relevant and nonredundant variables, address their predictive values, as well as the predictive value of the full variable set. Ten variable clusters and six subject clusters were identified, which differed and overlapped with previous clusters. Traditionally defined severe asthmatics distributed through subject Clusters 3–6. Cluster 4 identified early onset allergic asthmatics with low lung function and eosinophilic inflammation. Later onset, mostly severe asthmatics with nasal polyps and eosinophilia characterized Cluster 5. Cluster 6 asthmatics manifested persistent inflammation in blood and bronchoalveolar lavage and exacerbations despite high systemic corticosteroid use and side effects. Age of asthma onset, quality of life, symptoms, medications and health care utilization were some of the 51 nonredundant variables distinguishing subject clusters. These 51 variables classified test cases with 88% accuracy, compared to 93% accuracy with all 112 variables. The unsupervised machine learning approaches used here provide unique insights into disease, confirming other approaches while revealing novel additional phenotypes.
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影响因子：
24.3
作者：
Just, Jocelyne;Gouvis-Echraghi, Rahele;Annesi-Maesano, Isabella
通讯作者：
Annesi-Maesano, Isabella
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
14.2
作者：
Miranda, C;Busacker, A;Wenzel, SE
通讯作者：
Wenzel, SE
影响因子：
5.8
作者：
Troyanskaya, O;Cantor, M;Altman, RB
通讯作者：
Altman, RB
影响因子：
2.7
作者：
WELCH, BL
通讯作者：
WELCH, BL