ArticleScientific reports2021
An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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
6 citing papers in PubMed.
- Designing an explainable algorithm based on XGBoost and genetic algorithm for predicting hospitalization needs of COVID-19 patients.Scientific reports · 2026Article
- COVID-19 Clinical Predictors in Patients Treated via a Telemedicine Platform in 2022.Tropical medicine and infectious disease · 2025Article
- Magnesium Is a Vital Ion in the Body-It Is Time to Consider Its Supplementation on a Routine Basis.Clinics and practice · 2024Review
- Importance of Magnesium Status in COVID-19.Biology · 2023Review
- Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach.PloS one · 2023Article
- Predictive modeling for COVID-19 readmission risk using machine learning algorithms.BMC medical informatics and decision making · 2022Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study.
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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 Socratic graph.