%0 Journal Article
%A Walders, Julia
%A Wetz, Sophie
%A Costa, Ana Sofia
%A Hofmann, Anna
%A Schulz, Jörg B
%A Reetz, Kathrin
%A Dadsena, Ravi
%T Longitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers.
%J Scientific reports
%V 16
%N 1
%@ 2045-2322
%C [London]
%I Springer Nature
%M DZNE-2026-00200
%P 6517
%D 2026
%X Post-COVID-19 condition (PCC) manifests with prolonged, heterogeneous symptoms challenging both, diagnosis and therapeutic management. This three-year longitudinal study analyzed data from 93 adults (mean age of 48.9 ± 14.0, 60 female) after confirmed SARS-CoV-2 infection. Every follow-up visit included clinical, neuropsychological, and laboratory assessments, capturing multidimensional indicators of patient health. A machine learning framework was implemented to classify temporal stage of patient health status, identify visit-specific predictive markers, and manage incomplete data using both native handling in tree-based models and explicit imputation techniques. Gradient boosting methods consistently achieved the best performance across all visit comparisons, achieving F1-scores close to or above 90
%K Humans
%K Machine Learning
%K COVID-19: complications
%K COVID-19: psychology
%K Female
%K Middle Aged
%K Longitudinal Studies
%K Male
%K Biomarkers
%K Adult
%K SARS-CoV-2: isolation & purification
%K Neuropsychological Tests
%K Aged
%K Clinical biomarkers (Other)
%K Long COVID-19 (Other)
%K Longitudinal data (Other)
%K Machine learning (Other)
%K Predictive modeling (Other)
%K Biomarkers (NLM Chemicals)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:41690938
%R 10.1038/s41598-026-37635-3
%U https://pub.dzne.de/record/285258