Correction: A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms

May 7, 2026PLoS ONEOpen Access

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Key Points

To explore how machine learning can identify early symptom clusters in COVID-19 patients and their associations with clinical outcomes.
Applied a machine learning model to analyze symptom data from COVID-19 patients.
Classified patients into distinct early-symptom clusters by phenotype.
Investigated correlation of these clusters with hospitalization and prolonged symptoms.
Identified distinct symptom clusters that correlated with increased hospitalization rates.
Found that certain symptom clusters were linked to a failure to return to normal activities.
Demonstrated significant associations between specific phenotypes and prolonged COVID-19 symptoms.

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Identifies symptom cluster phenotypes correlating with hospitalization and prolonged symptoms, suggesting potential therapeutic targets.

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