Acute respiratory distress syndrome (ARDS) is a type of respiratory failure characterized by bilateral pulmonary infiltrates that cannot be explained entirely by cardiogenic pulmonary edema. ARDS is the primary cause of mortality in COVID-19 patients and one of the leading causes of morbidity and mortality in ICUs. Despite its significance and prevalence, the detection of ARDS remains highly variable and inconsistent. In this work, we develop a tool to automate the diagnosis of ARDS based on the Berlin definition to increase the accuracy of ARDS detection using electronic health record (EHR) fields. ARDSFlag applies machine learning (ML) and natural language processing (NLP) techniques to evaluate Berlin criteria by incorporating structured and unstructured data. The output is the ARDS diagnosis, onset time, and severity. We have also developed a visualization that helps clinicians efficiently assess ARDS criteria retrospectively and in real time. The method includes separate text classifiers trained using large training sets to find evidence of bilateral infiltrates in radiology reports (accuracy of 91.9%+-0.5%) and heart failure/fluid overload in radiology reports (accuracy 86.1%+-0.5%) and echocardiogram notes (accuracy 98.4%+-0.3%). A holdout set of 300 cases, which was blindly and independently labeled for ARDS by two groups of clinicians, shows that the algorithm generates an overall accuracy of 89.0%, with a specificity of 91.7%, recall of 80.3%, and precision of 75.0%. Compared with two other ARDS identification methods used in the literature, ARDSFlag shows higher performance in all accuracy measures (an increase of 25.5% in overall accuracy, 6.5% in specificity, 44.2% in recall, 31.7% in precision, and 38.20% in F1-score over the best of the two detection methods).

The authors have declared no competing interest.

This work was supported by philanthropic funds to the Feinstein Center for Health Outcomes and Innovation Research. The funding source did not control any aspect of the study and did not review the results. All authors had full access to the full data in the study and accept the responsibility for submitting it for publication.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

We used the MIMIC-III database which is publicly available (refer to https://mimic.mit.edu/docs/gettingstarted/ for instructions).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

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The 400 admissions that were manually labeled for ARDS are available in supplementary files. The data supporting this study's findings are available from the corresponding author upon reasonable request. The MIMIC-III database is publicly available (refer to https://mimic.mit.edu/docs/gettingstarted/ for instructions).