ArticlePatterns (New York, N.Y.)2020
A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics.
Article in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 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
12 citing papers in PubMed, 24 citations in OpenAlex.
- Time series analysis of dengue incidence and its association with meteorological risk factors in Bangladesh.PloS one · 2025Article
- Deep learning infused SIRVD model for COVID-19 prediction: XGBoost-SIRVD-LSTM approach.Frontiers in medicine · 2024Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- COVID-19 machine learning model predicts outcomes in older patients from various European countries, between pandemic waves, and in a cohort of Asian, African, and American patients.PLOS digital health · 2022Article
- An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples.Sensors (Basel, Switzerland) · 2022Article
- A machine learning model for predicting deterioration of COVID-19 inpatients.Scientific reports · 2022Article
- Exploring the Role of Innate Lymphocytes in the Immune System of Bats and Virus-Host Interactions.Viruses · 2022Review
- Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh.PLOS global public health · 2022Article
- A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers.PloS one · 2022Article
- Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms.Results in physics · 2021Article
- Influential Usage of Big Data and Artificial Intelligence in Healthcare.Computational and mathematical methods in medicine · 2021Review
- Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada.Frontiers in public health · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.
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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.