ArticleComputational and structural biotechnology journal2021
Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Prevalence of comorbid tuberculosis amongst COVID-19 patients: A rapid review and meta-analysis.International journal of clinical practice · 2021Pooled it
- The association of smoking status with SARS-CoV-2 infection, hospitalization and mortality from COVID-19: a living rapid evidence review with Bayesian meta-analyses (version 7).Addiction (Abingdon, England) · 2021Pooled it
- Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis.Scientific reports · 2025Article
- Diagnostic screening of COVID-19 based on multiomics data by high-resolution mass spectrometry (MALDI (+)-TOF MS and ESI(±)-Orbitrap MS).Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Temporal Radiographic Trajectory and Clinical Outcomes in COVID-19 Pneumonia: A Longitudinal Study.Journal of Korean medical science · 2025Article
- Subtle changes on electrocardiogram in severe patients with COVID-19 may be predictors of treatment outcome.Frontiers in artificial intelligence · 2025Article
- Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms.Scientific reports · 2024Article
- A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data.Diagnostics (Basel, Switzerland) · 2024Article
- Machine learning-aided algorithm design for prediction of severity from clinical, demographic, biochemical and immunological parameters: Our COVID-19 experience from the pandemic.Journal of family medicine and primary care · 2024Article
- Article
- The Diagnostic, Prognostic, and Therapeutic Potential of Cell-Free DNA with a Special Focus on COVID-19 and Other Viral Infections.International journal of molecular sciences · 2023Review
- Retrospective Post-Hospitalisation COVID-19 Mortality Risk Assessment of Patients in South Africa.European journal of investigation in health, psychology and education · 2023Article
- Multidimensional analysis of immune cells from COVID-19 patients identified cell subsets associated with the severity at hospital admission.PLoS pathogens · 2023Article
- CVD22: Explainable artificial intelligence determination of the relationship of troponin to D-Dimer, mortality, and CK-MB in COVID-19 patients.Computer methods and programs in biomedicine · 2023Article
- Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features.Journal of thoracic disease · 2023Article
- Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach.PloS one · 2023Article
- Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay.Sensors (Basel, Switzerland) · 2022Article
- Machine learning approach for automated predicting of COVID-19 severity based on clinical and paraclinical characteristics: Serum levels of zinc, calcium, and vitamin D.Clinical nutrition ESPEN · 2022Article
- The evaluation of the immune status of COVID-19 recovered subjects with persistent abnormal lung CT after one year: A longitudinal cohort study.International immunopharmacology · 2022Article
- A Comprehensive Review of Machine Learning Used to Combat COVID-19.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
34 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.
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