  In this work, we utilize Machine Learning for early recognition of patients
at high risk of acute respiratory distress syndrome (ARDS), which is critical
for successful prevention strategies for this devastating syndrome. The
difficulty in early ARDS recognition stems from its complex and heterogenous
nature. In this study, we integrate knowledge of the heterogeneity of ARDS
patients into predictive model building. Using MIMIC-III data, we first apply
latent class analysis (LCA) to identify homogeneous sub-groups in the ARDS
population, and then build predictive models on the partitioned data. The
results indicate that significantly improved performances of prediction can be
obtained for two of the three identified sub-phenotypes of ARDS. Experiments
suggests that identifying sub-phenotypes is beneficial for building predictive
model for ARDS.
