ArticlePloS one2021
A machine learning based exploration of COVID-19 mortality risk.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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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
32 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review.Frontiers in public health · 2023Pooled it
- A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort.Computational biology and chemistry · 2022Pooled it
- COVID-19 vaccine information disorder in Chile: a quantitative analysis of fact-checked articles.Frontiers in public health · 2025Article
- Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID.International journal of molecular sciences · 2024Article
- Online COVID-19 diagnosis prediction using complete blood count: an innovative tool for public health.BMC public health · 2023Article
- The Role of Aspartate Aminotransferase-to-Lymphocyte Ratio Index (ALRI) in Predicting Mortality in SARS-CoV-2 Infection.Microorganisms · 2023Article
- Exploring the clinical benefit of ventilation therapy across various patient groups with COVID-19 using real-world data.Scientific reports · 2023Article
- Racial/Ethnic Differences in Risk Factors Associated With Severe COVID-19 Among Older Adults With ADRD.Journal of the American Medical Directors Association · 2023Observational
- Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records.BMC bioinformatics · 2023Article
- Predicting the mortality of patients with Covid-19: A machine learning approach.Health science reports · 2023Article
- Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020-2022.Diagnostics (Basel, Switzerland) · 2023Article
- Selected Predictors of COVID-19 Mortality in the Hospitalised Patient Population in a Single-Centre Study in Poland.Healthcare (Basel, Switzerland) · 2023Article
- Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study.Scientific reports · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022.PloS one · 2023Article
- Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida.Frontiers in digital health · 2023Article
- A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity.Frontiers in big data · 2023Article
- 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
- CAD systems for COVID-19 diagnosis and disease stage classification by segmentation of infected regions from CT images.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients' day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, non-invasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage.
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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.