ArticleIEEE journal of biomedical and health informatics2025
RespBERT: A Multi-Site Validation of a Natural Language Processing Algorithm, of Radiology Notes to Identify Acute Respiratory Distress Syndrome (ARDS).
Article in IEEE journal of biomedical and health informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The regulation of artificial intelligence in intensive care units: from narrow tools to generalist systems.NPJ digital medicine · 2026Review
- Nanomedicine for Acute Respiratory Distress Syndrome: From Pathophysiological Mechanisms and Disease Heterogeneity to Precision Therapy.International journal of nanomedicine · 2026Review
- Bedside ventilatory settings guided by respiratory mechanics in acute respiratory distress syndrome.Annals of intensive care · 2025Review
- Large Language Models in Critical Care Medicine: Scoping Review.JMIR medical informatics · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Applications of generative artificial intelligence in outcome prediction in intensive care medicine-a scoping review.Frontiers in digital health · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Acute respiratory distress syndrome (ARDS) is a severe organ dysfunction associated with significant mortality and morbidity among critically ill patients admitted to the Intensive Care Unit (ICU). The etiology related to ARDS can be highly heterogeneous, with infection or trauma as the most common associations. The Berlin criteria, the current gold standard for ARDS diagnosis, often necessitates manual adjudication of chest radiographs, limiting automation tools. ARDS diagnosis relies on the presence of bilateral infiltrates on radiographs, which is often not available in Electronic Medical Records (EMRs). Automated identification of radiological evidence would facilitate a comprehensive study of the syndrome, eliminating the need for costly individual image inspections by physicians. Radiological reports enable Natural Language Processing (NLP) to assess lung status and evaluate imaging criteria. We developed a NLP pipeline to analyze radiology notes of 362 patients satisfying sepsis-3 criteria from the EMR for possible ARDS diagnosis using BERT model for classification. These classification models showed F1-score of 74.5% and 64.22% for Emory and Grady dataset respectively.
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Registered trials
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