Topic-to-Topic Modeling for COVID-19 Mortality

We examine a cohort of 4307 COVID-19 case fatalities from a de-identified national registry in the U.S. using Latent Dirichlet Allocation and group each patient by topic based on their pre-existing conditions in the years prior to infection and again during the last three weeks of life. We show that certain pre-existing condition topics have strong associations with certain COVID-19 mortality topics suggesting that the major clinical pathways leading to COVID-19 death may be through failures of already weakened organ systems. We then explore the demographics for these groups and generate several insights and hypotheses.