文章基本信息

标题：Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential
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作者：David Greenwood ; Thomas Taverner ; Nicola J. Adderley 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：7
页码：1-24
DOI：10.1016/j.isci.2022.104480
语种：English
出版社：Elsevier
摘要：SummaryClinical outcomes for patients with COVID-19 are heterogeneous and there is interest in defining subgroups for prognostic modeling and development of treatment algorithms. We obtained 28 demographic and laboratory variables in patients admitted to hospital with COVID-19. These comprised a training cohort (n = 6099) and two validation cohorts during the first and second waves of the pandemic (n = 996; n = 1011). Uniform manifold approximation and projection (UMAP) dimension reduction and Gaussian mixture model (GMM) analysis was used to define patient clusters. 29 clusters were defined in the training cohort and associated with markedly different mortality rates, which were predictive within confirmation datasets. Deconvolution of clinical features within clusters identified unexpected relationships between variables. Integration of large datasets using UMAP-assisted clustering can therefore identify patient subgroups with prognostic information and uncovers unexpected interactions between clinical variables. This application of machine learning represents a powerful approach for delineating disease pathogenesis and potential therapeutic interventions.Graphical abstractDisplay OmittedHighlights•UMAP-assisted clustering can define subgroups of acute COVID-19 patients•These clusters could be validated in independent patient cohorts•Cluster assignation on the day of admission was predictive of 28-day mortality•Clusters uncover clinical interactions that may to guide treatment approachesViral microbiology; Bioinformatics; Medical informatics