@article{Morales2025,
title={Open-source computational pipeline flags instances of acute respiratory distress syndrome in mechanically ventilated adult patients},
author={Morales FL, 
Xu F, 
Lee HA, 
Tejedor Navarro H, 
Bechel MA, 
Cameron EL, 
Kelso J, 
Weiss CH,
Amaral LAN },
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.},
journal={Nature Communications},
volume={16},
doi={10.1038/s41467-025-61418-5},
year={2025},
month={JUL},
day={23},
pages={6787},
}