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Susanne Poick

Schleswig-Holstein University - Internal Medicine Department I

Germany

SCHOLARLY PAPERS

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Scholarly Papers (1)

1.

Long-Term Trends in Post-COVID Severity: A Machine Learning Analysis from the POP/COVIDOM cohort of the German NAPKON Cohort Network

Number of pages: 31 Posted: 05 Sep 2025
Julius-Maximilians-Universität, Julius-Maximilians-University Wuerzburg, Internal Medicine Department I, University Hospital Schleswig Holstein, Campus Kiel, University of Kiel, Universitätsklinikum Schleswig-Holstein, Christian-Albrechts-Universität zu Kiel, Klinik für Innere Medizin I, University of Cologne, University of Würzburg, University of Cologne, University of Greifswald - University Hospital Greifswald, University Hospital of Schleswig-Holstein, Institute of Medical Informatics and Statistics, Kiel University, University Medical Center Schleswig-Holstein Campus Kiel, University of Kiel, Schleswig-Holstein University - Internal Medicine Department I, Institute of Clinical Epidemiology and Biometry, University of Würzburg, Germany, University Hospital Würzburg, Department for Medicine I and Comprehensive Heart Failure Center, German Center for Infection Research (DZIF), THM Technische Hochschule Mittelhessen, Free University of Berlin (FUB), Free University of Berlin (FUB), Institute of Social Medicine, Epidemiology and Health Economics, Charité-Universitätsmedizin Berlin, Berlin, Germany, Schleswig-Holstein University - Internal Medicine Department I, Schleswig-Holstein University - Internal Medicine Department I, Centre of Mental Health, University Hospital - Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg and Independent
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Abstract:

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COVID-19, Post-acute sequelae of SARS-CoV-2 infection (PASC), Post-COVID Syndrome, Long COVID, Fatigue, Machine-Learning, elastic net regression, symptom trajectories