ArticleInternational journal of medical sciences2021
Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables.
Article in International journal of medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of artificial intelligence in predicting the prognossis of severe COVID-19: a systematic review and meta-analysis.Frontiers in public health · 2024Pooled it
- Impact of asthma on COVID-19 mortality in the United States: Evidence based on a meta-analysis.International immunopharmacology · 2022Pooled it
- Machine learning-based prediction of intensive care unit admission in COVID-19 patients presenting with mild respiratory failure.Frontiers in medicine · 2026Article
- Calculating the Risk of Admission to Intensive Care Units in COVID-19 Patients Using Machine Learning.Journal of clinical medicine · 2025Article
- Drug utilisation patterns & clinical outcomes in hospitalised COVID-19 patients: A geospatial & machine learning approach.The Indian journal of medical research · 2025Article
- Predicting Severe Respiratory Failure in Patients with COVID-19: A Machine Learning Approach.Journal of clinical medicine · 2024Article
- Artificial intelligence for COVID-19 mortality prediction: improvement of risk stratification and clinical decision-making.Internal and emergency medicine · 2023Article
- Patients with prediabetes are at greater risk of developing diabetes 5 months postacute SARS-CoV-2 infection: a retrospective cohort study.BMJ open diabetes research & care · 2023Article
- Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study.Journal of business research · 2023Article
- Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2023Article
- Longitudinal medical subspecialty follow-up of critically and non-critically ill hospitalized COVID-19 survivors up to 24 months after discharge.Internal and emergency medicine · 2023Article
- Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China.Frontiers in medicine · 2023Article
- Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients.Biomedical engineering online · 2022Article
- Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients.Annals of operations research · 2022Article
- Long-short-term memory machine learning of longitudinal clinical data accurately predicts acute kidney injury onset in COVID-19: a two-center study.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022Article
- WMR-DepthwiseNet: A Wavelet Multi-Resolution Depthwise Separable Convolutional Neural Network for COVID-19 Diagnosis.Diagnostics (Basel, Switzerland) · 2022Article
- Longitudinal prediction of hospital-acquired acute kidney injury in COVID-19: a two-center study.Infection · 2022Article
- A novel method for handling pre-existing conditions in multivariate prediction model development for COVID-19 death in the Department of Veterans Affairs.Biology methods & protocols · 2022Article
- A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19.Diagnostics (Basel, Switzerland) · 2021Article
- Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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.