Finding Long-COVID: Temporal Topic Modeling of Electronic Health Records from the N3C and RECOVER Programs

    Shawn T. O’Neil, Charisse Madlock‐Brown, Kenneth J. Wilkins, Ryan McGrath, Hannah Davis, Gina Assaf, Hannah Wei, Parya Zareie, Evan French, Johanna Loomba, Julie A. McMurry, A Zhou, Christopher G Chute, Richard A. Moffitt, Emily Pfaff, Yun Jae Yoo, Peter J. Leese, Robert F Chew, Michael D. Lieberman, Melissa Haendel
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    TLDR Long-COVID has diverse, long-term health impacts, especially in young people.
    The study utilized electronic health records from the N3C and RECOVER programs to identify and analyze conditions associated with Long-COVID (PASC) using temporal topic modeling. By examining data from over 14 million patients, the researchers identified 213 significant conditions, including non-scarring alopecia and telogen effluvium, particularly in pediatric and adolescent PASC patients. The study highlighted the diverse and long-term health impacts of COVID-19, emphasizing the importance of comprehensive data analysis and improved diagnostics for understanding and treating Long-COVID.
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