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000285258 1001_ $$aWalders, Julia$$b0
000285258 245__ $$aLongitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers.
000285258 260__ $$a[London]$$bSpringer Nature$$c2026
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000285258 520__ $$aPost-COVID-19 condition (PCC) manifests with prolonged, heterogeneous symptoms challenging both, diagnosis and therapeutic management. This three-year longitudinal study analyzed data from 93 adults (mean age of 48.9 ± 14.0, 60 female) after confirmed SARS-CoV-2 infection. Every follow-up visit included clinical, neuropsychological, and laboratory assessments, capturing multidimensional indicators of patient health. A machine learning framework was implemented to classify temporal stage of patient health status, identify visit-specific predictive markers, and manage incomplete data using both native handling in tree-based models and explicit imputation techniques. Gradient boosting methods consistently achieved the best performance across all visit comparisons, achieving F1-scores close to or above 90%. Classification performance improved with greater time intervals between visits, suggesting progressive divergence in patient phenotypes over time. For discriminating follow-up stages, inflammatory markers emerged as the most informative predictors, followed by SARS-CoV-2 antibody levels and neuropsychiatric measures for fatigue and cognitive performance. Interpretability analyses using SHAP and LIME confirmed the contribution of these features, while revealing shifts in feature relevance across years. These findings highlight the utility of machine learning in characterizing follow-up stage separability in PCC and offer clinically interpretable insights that prioritize immune and neuropsychological measures for monitoring and risk-stratified follow-up.
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000285258 650_7 $$2Other$$aClinical biomarkers
000285258 650_7 $$2Other$$aLong COVID-19
000285258 650_7 $$2Other$$aLongitudinal data
000285258 650_7 $$2Other$$aMachine learning
000285258 650_7 $$2Other$$aPredictive modeling
000285258 650_7 $$2NLM Chemicals$$aBiomarkers
000285258 650_2 $$2MeSH$$aHumans
000285258 650_2 $$2MeSH$$aMachine Learning
000285258 650_2 $$2MeSH$$aCOVID-19: complications
000285258 650_2 $$2MeSH$$aCOVID-19: psychology
000285258 650_2 $$2MeSH$$aFemale
000285258 650_2 $$2MeSH$$aMiddle Aged
000285258 650_2 $$2MeSH$$aLongitudinal Studies
000285258 650_2 $$2MeSH$$aMale
000285258 650_2 $$2MeSH$$aBiomarkers
000285258 650_2 $$2MeSH$$aAdult
000285258 650_2 $$2MeSH$$aSARS-CoV-2: isolation & purification
000285258 650_2 $$2MeSH$$aNeuropsychological Tests
000285258 650_2 $$2MeSH$$aAged
000285258 7001_ $$aWetz, Sophie$$b1
000285258 7001_ $$aCosta, Ana Sofia$$b2
000285258 7001_ $$0P:(DE-2719)2814244$$aHofmann, Anna$$b3$$udzne
000285258 7001_ $$aSchulz, Jörg B$$b4
000285258 7001_ $$aReetz, Kathrin$$b5
000285258 7001_ $$0P:(DE-2719)9001926$$aDadsena, Ravi$$b6$$udzne
000285258 773__ $$0PERI:(DE-600)2615211-3$$a10.1038/s41598-026-37635-3$$gVol. 16, no. 1, p. 6517$$n1$$p6517$$tScientific reports$$v16$$x2045-2322$$y2026
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