ArticleJournal of clinical medicine2022
A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- The Role of Kidney Function in Predicting COVID-19 Severity and Clinical Outcomes: A Retrospective Analysis.Infectious disease reports · 2025Article
- Article
- A systematic review of tools for predicting complications in patients with influenza-like illness.Heliyon · 2024Review
- Oxygen Requirement in Overweight/Obese Kidney Transplant Recipients with COVID-19: An Observational Cohort Study.Diagnostics (Basel, Switzerland) · 2023Article
- Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19.Frontiers in medicine · 2023Article
- Article
- Impact of Hypertension on COVID-19 Burden in Kidney Transplant Recipients: An Observational Cohort Study.Viruses · 2022Observational
Corrections and comments
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Authors and funding
15 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A machine learning approach is a useful tool for risk-stratifying patients with respiratory symptoms during the COVID-19 pandemic, as it is still evolving. We aimed to verify the predictive capacity of a gradient boosting decision trees (XGboost) algorithm to select the most important predictors including clinical and demographic parameters in patients who sought medical support due to respiratory signs and symptoms (RAPID RISK COVID-19). A total of 7336 patients were enrolled in the study, including 6596 patients that did not require hospitalization and 740 that required hospitalization. We identified that patients with respiratory signs and symptoms, in particular, lower oxyhemoglobin saturation by pulse oximetry (SpO
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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.