Observational studyChest2021
Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.
Observational study in Chest, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Optimizing Intubation Prediction in Pneumonia Patients: A Systematic Review and Meta-Analysis of Machine Learning Algorithms.Pulmonary medicine · 2026Pooled it
- Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment-implications for clinical practice.Frontiers in medical technology · 2025Pooled it
- Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: A systematic review and meta-analysis.Diabetes & metabolic syndromePooled it
- Artificial intelligence in mechanical ventilation: a narrative review of clinical applications and research gaps.Journal of thoracic disease · 2026Review
- Physiological Data Integration and Predictive Modeling in Intensive Care.Life (Basel, Switzerland) · 2026Review
- [Development and validation of an interpretable machine learning model for predicting reguirement of early invasive mechanical ventilation in patients with acute pancreatitis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Advocating the potential of artificial intelligence for syndrome discovery in syndromic surveillance systems: A scoping review.iScience · 2026Review
- Deep Learning for Early Prediction of Adverse Events in Intensive Care Units Using Electronic Health Records: A Methodological Review.Risk management and healthcare policy · 2026Review
- RMS: a ML-based system for ICU respiratory monitoring and resource planning.NPJ digital medicine · 2025Article
- The Alveolar Gas Monitor: An Alternative to Pulse Oximetry for the Noninvasive Assessment of Impaired Gas Exchange in Patients at Risk of Respiratory Deterioration.Journal of clinical medicine · 2025Article
- Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery.BMC anesthesiology · 2025Article
- Can we predict the future of respiratory failure prediction?Critical care (London, England) · 2025Review
- Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques.Scientific reports · 2025Article
- Adaptable graph neural networks design to support generalizability for clinical event prediction.Journal of biomedical informatics · 2025Article
- Use of machine learning models to predict mechanical ventilation, ECMO, and mortality in COVID-19.Frontiers in artificial intelligence · 2025Article
- Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.PloS one · 2025Article
- Artificial Intelligence in the Management of Patients with Respiratory Failure Requiring Mechanical Ventilation: A Scoping Review.Journal of clinical medicine · 2024Article
- Development, deployment, and continuous monitoring of a machine learning model to predict respiratory failure in critically ill patients.JAMIA open · 2024Article
- Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.Scientific reports · 2024Article
- Machine learning modelling for predicting the utilization of invasive and non-invasive ventilation throughout the ICU duration.Healthcare technology letters · 2024Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundObjective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation (MV) may aid in delivering timely treatment. RESEARCH QUESTION: Can a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24 h in advance? STUDY DESIGN AND
methodsWe trained and externally validated a transparent DL algorithm to predict the future need for MV in hospitalized patients, including those with COVID-19, using commonly available data in electronic health records. Additionally, commonly used clinical criteria (heart rate, oxygen saturation, respiratory rate, Fio
resultsWe obtained data from more than 30,000 ICU patients (including more than 700 patients with COVID-19) from two academic medical centers. The performance of the model with a 24-h prediction horizon at the development and validation sites was comparable (AUC, 0.895 vs 0.882, respectively), providing significant improvement over traditional clinical criteria (P < .001). Prospective validation of the algorithm among patients with COVID-19 yielded AUCs in the range of 0.918 to 0.943.
interpretationA transparent deep learning algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. Such an algorithm may help clinicians to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.