ArticleCritical care medicine2020
External Validation of an Acute Respiratory Distress Syndrome Prediction Model Using Radiology Reports.
Article in Critical care medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- An Electronic Health Record-Based Classifier for Moderate-to-Severe Acute Respiratory Distress Syndrome With Persistent Hypoxemia.Critical care medicine · 2026Observational
- Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.Critical care medicine · 2025Article
- RespBERT: A Multi-Site Validation of a Natural Language Processing Algorithm, of Radiology Notes to Identify Acute Respiratory Distress Syndrome (ARDS).IEEE journal of biomedical and health informatics · 2025Article
- Natural language processing in the intensive care unit: A scoping review.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2024Article
- Understanding clinical and biological heterogeneity to advance precision medicine in paediatric acute respiratory distress syndrome.The Lancet. Respiratory medicine · 2023Review
- Chinese experts' consensus on the application of intensive care big data.Frontiers in medicine · 2023Article
- Development and validation of a predictive model for early diagnosis of neonatal acute respiratory distress syndrome based on the Montreux definition.Frontiers in pediatrics · 2023Article
- Comparison of radiographic pneumothorax and pneumomediastinum in COVID-19 vs. non-COVID-19 acute respiratory distress syndrome.Intensive care medicine · 2022Article
- Identifying ARDS using the Hierarchical Attention Network with Sentence Objectives Framework.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesAcute respiratory distress syndrome is frequently under recognized and associated with increased mortality. Previously, we developed a model that used machine learning and natural language processing of text from radiology reports to identify acute respiratory distress syndrome. The model showed improved performance in diagnosing acute respiratory distress syndrome when compared to a rule-based method. In this study, our objective was to externally validate the natural language processing model in patients from an independent hospital setting.
designSecondary analysis of data across five prospective clinical studies.
settingAn urban, tertiary care, academic hospital. PATIENTS: Adult patients admitted to the medical ICU and at-risk for acute respiratory distress syndrome.
interventionsNone. MEASUREMENTS AND MAIN
resultsThe natural language processing model was previously derived and internally validated in burn, trauma, and medical patients at Loyola University Medical Center. Two machine learning models were examined with the following text features from qualifying radiology reports: 1) word representations (n-grams) and 2) standardized clinical named entity mentions mapped from the National Library of Medicine Unified Medical Language System. The models were externally validated in a cohort of 235 patients at the University of Chicago Medicine, among which 110 (47%) were diagnosed with acute respiratory distress syndrome by expert annotation. During external validation, the n-gram model demonstrated good discrimination between acute respiratory distress syndrome and nonacute respiratory distress syndrome patients (C-statistic, 0.78; 95% CI, 0.72-0.84). The n-gram model had a higher discrimination for acute respiratory distress syndrome when compared with the standardized named entity model, although not statistically significant (C-statistic 0.78 vs 0.72; p = 0.09). The most important features in the model had good face validity for acute respiratory distress syndrome characteristics but differences in frequencies did occur between hospital settings.
conclusionsOur computable phenotype for acute respiratory distress syndrome had good discrimination in external validation and may be used by other health systems for case-identification. Discrepancies in feature representation are likely due to differences in characteristics of the patient cohorts.
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