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

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.