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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月23日

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  • Félix L. Morales,
  • Feihong Xu,
  • Hyojun Ada Lee,
  • Helio Tejedor Navarro,
  • Meagan A. Bechel,
  • Eryn L. Cameron,
  • Jesse Kelso,
  • Curtis H. Weiss,
  • Luís A. Nunes Amaral

摘要

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.