Open-source computational pipeline flags instances of acute respiratory distress syndrome in mechanically ventilated adult patients.

ABSTRACT: Physicians in critical care settings face information overload and decision fatigue, contributing to under-recognition of acute respiratory distress syndrome, which affects over 10% of intensive care patients and carries over 40% mortality rate. We present a reproducible computational pipeline to automatically identify this condition retrospectively in mechanically ventilated adults. This computational pipeline operationalizes the Berlin Definition by detecting bilateral infiltrates from radiology reports and a pneumonia diagnosis from attending physician notes, using interpretable classifiers trained on labeled data. Here we show that our integrated pipeline achieves high performance-93.5% sensitivity and 17.4% false positive rate-when applied to a held-out and publicly-available dataset

SUBMITTER: Morales FL 

PROVIDER: S-EPMC12287280 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature