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Research classifies long COVID symptoms, revealing that frequency and severity enhance machine learning accuracy.
Summary Long COVID definitions based solely on symptom occurrence may reduce diagnostic specificity. Machine‐learning models incorporating symptom frequency and severity outperformed occurrence‐only models. Composite scoring achieved 90.12% accuracy compared with 88.73% for occurrence‐based scoring. Composite models required fewer predictive symptoms, indicating greater efficiency. Measuring symptom burden improves the precision of research classification for Long COVID.
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Jason et al. (2026) studied this question.
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