ArticleeLife2020
Early prediction of level-of-care requirements in patients with COVID-19.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- Hospitalised versus outpatient COVID-19 patients' background characteristics and comorbidities: A systematic review and meta-analysis.Reviews in medical virology · 2022Pooled it
- Artificial intelligence in mechanical ventilation: a narrative review of clinical applications and research gaps.Journal of thoracic disease · 2026Review
- Designing an explainable algorithm based on XGBoost and genetic algorithm for predicting hospitalization needs of COVID-19 patients.Scientific reports · 2026Article
- An interdisciplinary perspective of the built-environment microbiome.FEMS microbiology ecology · 2025Review
- Physical Inactivity Syndrome: A Risk Factor and Target for Intervention-A Narrative Review.Progress in rehabilitation medicine · 2025Review
- Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID.International journal of molecular sciences · 2024Article
- A GPT-based EHR modeling system for unsupervised novel disease detection.Journal of biomedical informatics · 2024Article
- Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics.Biomimetics (Basel, Switzerland) · 2024Article
- Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study.BMC research notes · 2024Article
- Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention.Current medical science · 2024Article
- ITNR: Inversion Transformer-based Neural Ranking for cancer drug recommendations.Computers in biology and medicine · 2024Article
- Performance of an AI algorithm during the different phases of the COVID pandemics: what can we learn from the AI and vice versa.European journal of radiology open · 2023Article
- Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.Scientific reports · 2023Article
- Machine learning algorithm for early-stage prediction of severe morbidity in COVID-19 pneumonia patients based on bio-signals.BMC pulmonary medicine · 2023Article
- 3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.Journal of digital imaging · 2023Article
- Early detection of deterioration in COVID-19 patients by continuous ward respiratory rate monitoring: a pilot prospective cohort study.Frontiers in medicine · 2023Article
- Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran.BMC medical research methodology · 2022Article
- A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2.BMC medical informatics and decision making · 2022Observational
- Social determinants of health and the prediction of missed breast imaging appointments.BMC health services research · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.
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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.