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D Zeiberg, T Prahlad, BK Nallamothu, TJ Iwashyna, J Wiens and MW Sjoding
Abstract
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 © can be applied at clinically relevant time points throughout a patient’s stay.
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