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Existing prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-limiting their suitability for driving clinical interventions. We sought to develop a machine learning approach for the prediction of ARDS that (a) leverages electronic health record (EHR) data, (b) is fully automated, and (c) can be applied at clinically relevant time points throughout a patient's stay.doi:10.1371/journal.pone.0214465 pmid:30921400 pmcid:PMC6438573 fatcat:ujymr6pf7bfgpmmd3uawvdrwem