ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019
Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- Can we predict the future of respiratory failure prediction?Critical care (London, England) · 2025Review
- Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort.BMJ health & care informatics · 2023Article
- Trends and opportunities in computable clinical phenotyping: A scoping review.Journal of biomedical informatics · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Lung-Protective Ventilation Over 6 Years at a Large Academic Medical Center: An Evaluation of Trends, Adherence, and Perceptions of Benefit.Critical care explorations · 2021Article
- 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
7 authors.
Funding
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Abstract
In this work, we utilize a combination of free-text and structured data to build Acute Respiratory Distress Syndrome(ARDS) prediction models and ARDS phenotype clusters. We derived 'Patient Context Vectors' representing patientspecific contextual ARDS risk factors, utilizing deep-learning techniques on ICD and free-text clinical notes data. The Patient Context Vectors were combined with structured data from the first 24 hours of admission, such as vital signs and lab results, to build an ARDS patient prediction model and an ARDS patient mortality prediction model achieving AUC of 90.16 and 81.01 respectively. The ability of Patient Context Vectors to summarize patients' medical history and current conditions is also demonstrated by the automatic clustering of ARDS patients into clinically meaningful phenotypes based on comorbidities, patient history, and presenting conditions. To our knowledge, this is the first study to successfully combine free-text and structured data, without any manual patient risk factor curation, to build real-time ARDS prediction models.
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