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In this work, we utilize Machine Learning for early recognition of patientsat high risk of acute respiratory distress syndrome (ARDS), which is criticalfor successful prevention strategies for this devastating syndrome. Thedifficulty in early ARDS recognition stems from its complex and heterogenousnature. In this study, we integrate knowledge of the heterogeneity of ARDSpatients into predictive model building. Using MIMIC-III data, we first applylatent class analysis (LCA) to identify homogeneous sub-groups in the ARDSpopulation, and then build predictive models on the partitioned data. Theresults indicate that significantly improved performances of prediction can beobtained for two of the three identified sub-phenotypes of ARDS. Experimentssuggests that identifying sub-phenotypes is beneficial for building predictivemodel for ARDS.

Using Latent Class Analysis to Identify ARDS Sub-phenotypes for Enhanced Machine Learning Predictive Performance