ArticlePeerJ2020
Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 4 of them syntheses that pooled it.
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The trial behind it
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Who cites it
62 citing papers in PubMed, 4 syntheses or guidelines pooled it, 113 citations in OpenAlex.
- The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Impact of asthma on COVID-19 mortality in the United States: Evidence based on a meta-analysis.International immunopharmacology · 2022Pooled it
- Does Famotidine Reduce the Risk of Progression to Severe Disease, Death, and Intubation for COVID-19 Patients? A Systemic Review and Meta-Analysis.Digestive diseases and sciences · 2021Pooled it
- Prognostic Significance of Computed Tomography Severity Score for Machine Learning Prediction of Intensive Care Unit Admission in COVID-19 Patients.Thoracic research and practice · 2026Article
- Machine learning-based prediction of intensive care unit admission in COVID-19 patients presenting with mild respiratory failure.Frontiers in medicine · 2026Article
- Smart emergency care: a narrative review of predictive machine learning models.Annals of translational medicine · 2025Review
- Increased Incidence of New-Onset Diabetic Retinopathy in Individuals with COVID-19 in an Underserved Urban Population in the Bronx.Diagnostics (Basel, Switzerland) · 2025Article
- Calculating the Risk of Admission to Intensive Care Units in COVID-19 Patients Using Machine Learning.Journal of clinical medicine · 2025Article
- Patients With Type 2 Diabetes Are at Greater Risk of Developing New Hypertension and Chronic Kidney Disease Following COVID-19.Journal of diabetes research · 2025Article
- Predicting Severe Respiratory Failure in Patients with COVID-19: A Machine Learning Approach.Journal of clinical medicine · 2024Article
- Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida.Diagnostics (Basel, Switzerland) · 2024Article
- O2 supplementation disambiguation in clinical narratives to support retrospective COVID-19 studies.BMC medical informatics and decision making · 2024Article
- Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables.Scientific reports · 2024Article
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- Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves.Viruses · 2023Article
- Combined systemic inflammatory indexes as reflectors of outcome in patients with COVID‑19 infection admitted to ICU.Inflammopharmacology · 2023Article
- Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.Scientific reports · 2023Article
- Predicting intensive care need for COVID-19 patients using deep learning on chest radiography.Journal of medical imaging (Bellingham, Wash.) · 2023Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients.
methodsThis retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC).
resultsThe top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760-0.785]) and 0.844 (95% CI [0.839-0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726-0.729]) and 0.848 (95% CI [0.847-0.849]), respectively.
conclusionsDeep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.
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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.