ArticlePloS one2019
Machine learning for patient risk stratification for acute respiratory distress syndrome.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07328997 (Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence), which is not on this map. Cited by 50 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence
Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Cataract surgery risk stratification models: a systematic review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Pooled it
- A systematic review of machine learning models for management, prediction and classification of ARDS.Respiratory research · 2024Pooled it
- Trial
- Deep knowledge-driven multi-modal fusion for diagnosis and prognosis of SI-ARDS.Communications medicine · 2026Article
- RMS: a ML-based system for ICU respiratory monitoring and resource planning.NPJ digital medicine · 2025Article
- Optimising time-limited trials in acute respiratory failure: a multicentre focused ethnography protocol.BMJ open · 2025Article
- Observational
- Multimodal Deep Learning for ARDS Detection.medRxiv : the preprint server for health sciences · 2025Article
- Open-source computational pipeline flags instances of acute respiratory distress syndrome in mechanically ventilated adult patients.Nature communications · 2025Article
- Impact of ventilatory and laboratory parameter trajectories on short-term survival in acute respiratory distress syndrome patients: a retrospective study using joint models.European journal of medical research · 2025Article
- Predicting mortality and risk factors of sepsis related ARDS using machine learning models.Scientific reports · 2025Article
- Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.Yonsei medical journal · 2025Article
- Probabilistic medical predictions of large language models.NPJ digital medicine · 2024Article
- Robust extraction of pneumonia-associated clinical states from electronic health records.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Characterizing the Use of Time-Limited Trials in Patients With Acute Respiratory Failure: A Prospective, Single-Center Observational Study.Critical care explorations · 2024Observational
- Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study.EClinicalMedicine · 2024Article
- Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets.IEEE open journal of engineering in medicine and biology · 2024Article
- Article
- 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
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundExisting prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-limiting their suitability for driving clinical interventions. We sought to develop a machine learning approach for the prediction of ARDS that (a) leverages electronic health record (EHR) data, (b) is fully automated, and (c) can be applied at clinically relevant time points throughout a patient's stay. METHODS AND
findingsWe trained a risk stratification model for ARDS using a cohort of 1,621 patients with moderate hypoxia from a single center in 2016, of which 51 patients developed ARDS. We tested the model in a temporally distinct cohort of 1,122 patients from 2017, of which 27 patients developed ARDS. Gold standard diagnosis of ARDS was made by intensive care trained physicians during retrospective chart review. We considered both linear and non-linear approaches to learning the model. The best model used L2-logistic regression with 984 features extracted from the EHR. For patients observed in the hospital at least six hours who then developed moderate hypoxia, the model achieved an area under the receiver operating characteristics curve (AUROC) of 0.81 (95% CI: 0.73-0.88). Selecting a threshold based on the 85th percentile of risk, the model had a sensitivity of 56% (95% CI: 35%, 74%), specificity of 86% (95% CI: 85%, 87%) and positive predictive value of 9% (95% CI: 5%, 14%), identifying a population at four times higher risk for ARDS than other patients with moderate hypoxia and 17 times the risk of hospitalized adults.
conclusionsWe developed an ARDS prediction model based on EHR data with good discriminative performance. Our results demonstrate the feasibility of a machine learning approach to risk stratifying patients for ARDS solely from data extracted automatically from the EHR.
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