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@ARTICLE{Walders:285258,
      author       = {Walders, Julia and Wetz, Sophie and Costa, Ana Sofia and
                      Hofmann, Anna and Schulz, Jörg B and Reetz, Kathrin and
                      Dadsena, Ravi},
      title        = {{L}ongitudinal modeling of {P}ost-{COVID}-19 condition over
                      three years: {A} machine learning approach using clinical,
                      neuropsychological, and fluid markers.},
      journal      = {Scientific reports},
      volume       = {16},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DZNE-2026-00200},
      pages        = {6517},
      year         = {2026},
      abstract     = {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 $90\%.$ Classification
                      performance improved with greater time intervals between
                      visits, suggesting progressive divergence in patient
                      phenotypes over time. For discriminating follow-up stages,
                      inflammatory markers emerged as the most informative
                      predictors, followed by SARS-CoV-2 antibody levels and
                      neuropsychiatric measures for fatigue and cognitive
                      performance. Interpretability analyses using SHAP and LIME
                      confirmed the contribution of these features, while
                      revealing shifts in feature relevance across years. These
                      findings highlight the utility of machine learning in
                      characterizing follow-up stage separability in PCC and offer
                      clinically interpretable insights that prioritize immune and
                      neuropsychological measures for monitoring and
                      risk-stratified follow-up.},
      keywords     = {Humans / Machine Learning / COVID-19: complications /
                      COVID-19: psychology / Female / Middle Aged / Longitudinal
                      Studies / Male / Biomarkers / Adult / SARS-CoV-2: isolation
                      $\&$ purification / Neuropsychological Tests / Aged /
                      Clinical biomarkers (Other) / Long COVID-19 (Other) /
                      Longitudinal data (Other) / Machine learning (Other) /
                      Predictive modeling (Other) / Biomarkers (NLM Chemicals)},
      cin          = {AG Jucker},
      ddc          = {600},
      cid          = {I:(DE-2719)1210001},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41690938},
      doi          = {10.1038/s41598-026-37635-3},
      url          = {https://pub.dzne.de/record/285258},
}