Back to search

Article

Machine Learning Analysis of Post-Acute COVID Symptoms Identifies Distinct Clusters, Severity Groups, and Trajectories

2025-11-17

Abstract excerpt

Questionnaires that capture patient-reported symptomatology provide low-cost but potentially high-value data for the de novo discovery of disease phenotype, severity, and responsiveness to intervention groupings within an umbrella condition. The availability of comprehensive electronic health records (EHRs) has nonetheless overshadowed the use of questionnaires data for symptom analysis in the context of COVID-19...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
d7320e75-e889-50c4-87c1-181b725ae58b
DOI
10.1101/2025.11.16.25340350
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Machine Learning Analysis of Post-Acute COVID Symptoms Identifies Distinct Clusters, Severity Groups, and TrajectoriesDOI 10.1101/2025.11.16.25340350
Select a neighboring publication to make it the new centre.