ArticlePloS one2023
A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms.
Article in PloS one, 2023. 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 17 papers.
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Who cites it
17 citing papers in PubMed, 17 citations in OpenAlex.
- Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.Cardiovascular toxicology · 2026Review
- A mixed-methods study comparing clinical and patient-reported perspectives on long COVID: insights from electronic health records and narrative journal entries.BMC infectious diseases · 2026Observational
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- Identifying subtypes of Long COVID: a systematic review.EClinicalMedicine · 2026Article
- Machine learning identifies clusters of multimorbidity among decedents with inflammatory bowel disease.Communications medicine · 2025Article
- Impact of long COVID phenotypes on quality of life following symptomatic omicron infection in Brazil: a machine learning analysis.BMC infectious diseases · 2025Observational
- Precision Symptom Phenotyping Identifies Early Clinical and Proteomic Predictors of Distinct COVID-19 Sequelae.The Journal of infectious diseases · 2025Article
- Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients.BMC pulmonary medicine · 2025Article
- Article
- Identifying central symptom clusters and correlates among post-COVID-19 pulmonary fibrosis patients: a network analysis.Frontiers in medicine · 2025Article
- Post-concussion symptom burden and dynamics: Insights from a digital health intervention and machine learning.PLOS digital health · 2025Article
- Temporal, spatial and demographic distributions characteristics of COVID-19 symptom clusters from chinese medicine perspective: a systematic cross-sectional study in China from 2019 to 2023.Chinese medicine · 2024Article
- Associations Between Acute COVID-19 Symptom Profiles and Long COVID Prevalence: Population-Based Cross-Sectional Study.JMIR public health and surveillance · 2024Article
- SARS-CoV-2 variant replacement constrains vaccine-specific viral diversification.Virus evolution · 2024Article
- Future applications of host direct therapies for infectious disease treatment.Frontiers in immunology · 2024Review
- SARS-CoV-2 infection is associated with self-reported post-acute neuropsychological symptoms within six months of follow-up.PloS one · 2024Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
31 authors at 14 institutions in 1 country.
Funding
Abstract
backgroundAccurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflammatory profiles.
methods1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative symptom scores (FLU-PRO Plus) were included in this analysis. We employed machine-learning approaches to identify symptom clusters and compared risk of hospitalization, long-term symptoms, as well as peak CRP and IL-6 concentrations.
resultsWe identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 ("Nasal cluster") is highly correlated with reporting runny/stuffy nose and sneezing, cluster 2 ("Sensory cluster") is highly correlated with loss of smell or taste, and cluster 3 ("Respiratory/Systemic cluster") is highly correlated with the respiratory (cough, trouble breathing, among others) and systemic (body aches, chills, among others) domain symptoms. Participants in the Respiratory/Systemic cluster were twice as likely as those in the Nasal cluster to have been hospitalized, and 1.5 times as likely to report that they had not returned-to-activities, which remained significant after controlling for confounding covariates (P < 0.01). Respiratory/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset (P = 0.03). We observed higher peak CRP and IL-6 in the Respiratory/Systemic cluster (P < 0.01).
conclusionsWe identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.
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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.