  Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an
emerging medical condition that has been observed in several patients with a
positive diagnosis for COVID-19. Historical Electronic Health Records (EHR)
like diagnosis codes, lab results and clinical notes have been analyzed using
deep learning and have been used to predict future clinical events. In this
paper, we propose an interpretable deep learning approach to analyze historical
diagnosis code data from the National COVID Cohort Collective (N3C) to find the
risk factors contributing to developing Long COVID. Using our deep learning
approach, we are able to predict if a patient is suffering from Long COVID from
a temporally ordered list of diagnosis codes up to 45 days post the first COVID
positive test or diagnosis for each patient, with an accuracy of 70.48\%. We
are then able to examine the trained model using Gradient-weighted Class
Activation Mapping (GradCAM) to give each input diagnoses a score. The highest
scored diagnosis were deemed to be the most important for making the correct
prediction for a patient. We also propose a way to summarize these top
diagnoses for each patient in our cohort and look at their temporal trends to
determine which codes contribute towards a positive Long COVID diagnosis.
