Figure 1: Experimental design summary.

(1) We trained an LDA topic model on a broad set of N3C patient data, tuning and evaluating the model with a held-out validation set using the UCI coherence metric. (2) Within a separate held-out assessment patient set, we defined three cohorts: PASC (patients with Long COVID), COVID (COVID-19 only), and Control (neither). For these patients we defined a 1-year pre-infection phase 6-month post-infection phase, utilizing a mock infection date for Control patients. (3) For the top 20 conditions per topic, we assessed new onset rates for COVID and PASC patients compared to Controls in the post-infection phase. (4) Finally, we defined per-topic logistic models, with outcome variables as the topic model’s assigned probabilities to individual patient phase data. Model coefficients then relate patient demographics, cohort, infection phase, and combinations of these factors to topic assignment for further study.