ArticleNPJ digital medicine2021
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 2 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
66 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem.Medical image analysis · 2021Pooled it
- Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study.The British journal of radiology · 2022Trial
- Use of Machine-Learning Models to Identify Clinical Features Associated With A Future Clinical Worsening Event in Patients With Pulmonary Arterial Hypertension.Pulmonary circulation · 2026Article
- PET/CT-based quantification of intratumoral heterogeneity for predicting pathological complete response in breast cancer patients treated with neoadjuvant chemotherapy: A multicentre study.European journal of nuclear medicine and molecular imaging · 2026Article
- Real-time prediction of cardiorespiratory deterioration during paediatric critical care transport using interpretable machine learning.PLOS digital health · 2026Article
- Stratified prediction of HER2 status in breast cancer by integrating intratumoral and peritumoral radiomics from DCE-MRI.BMC cancer · 2026Article
- Early Insights Among Emergency Medicine Physicians on Artificial Intelligence: A National, Convenience-sample Survey of the American College of Emergency Physicians.Journal of the American College of Emergency Physicians open · 2026Article
- Evaluation of ChatGPT-5 for Automated ASPECTS Assessment on Non-Contrast CT in Acute Ischemic Stroke.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence-assisted prediction of Demodex mite density in facial erythema.Scientific reports · 2025Article
- Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights.Scientific reports · 2025Article
- Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls.Cureus · 2025Review
- Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models.Narra J · 2025Article
- Prediction models based on machine learning algorithms for COVID-19 severity risk.BMC public health · 2025Article
- Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- Unveiling GPT-4V's hidden challenges behind high accuracy on USMLE questions: Observational Study.Journal of medical Internet research · 2025Observational
- Deep learning to predict progression independent of relapse activity at a first demyelinating event.Brain communications · 2025Article
- Breaking new ground: machine learning enhances survival forecasts in hypercapnic respiratory failure.Frontiers in medicine · 2025Article
- Predicting Severe Respiratory Failure in Patients with COVID-19: A Machine Learning Approach.Journal of clinical medicine · 2024Article
- Corticosteroid treatment prediction using chest X-ray and clinical data.Computational and structural biotechnology journal · 2024Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
- Update of
Authors and funding
20 authors.
Funding
Abstract
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
Identifiers
What OpenQuestion holds
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.