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

Félix L. Morales, Feihong Xu, Hyojun Ada Lee, Helio Tejedor Navarro, Meagan A. Bechel, Eryn L. Cameron, Jesse Kelso, Curtis H. Weiss () and Luís A. Nunes Amaral ()
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Félix L. Morales: Northwestern University
Feihong Xu: Northwestern University
Hyojun Ada Lee: Northwestern University
Helio Tejedor Navarro: Northwestern University
Meagan A. Bechel: Northwestern University
Eryn L. Cameron: Endeavor Health
Jesse Kelso: Endeavor Health
Curtis H. Weiss: Northwestern University
Luís A. Nunes Amaral: Northwestern University

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: 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 from an external hospital. This substantially exceeds the 22.6% documentation rate observed in the same cohort. These results demonstrate that our automated adjudication pipeline can accurately identify an under-diagnosed condition in critical care and may support timely recognition and intervention through integration with electronic health records.

Date: 2025
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DOI: 10.1038/s41467-025-61418-5

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