Topic-to-Topic Modeling for COVID-19 Mortality
- Resource Type
- Conference
- Authors
- Humpherys, Jeffrey; Halwani, Ahmad; Jones, Barbara E; Jones, Makoto M; Samore, Matthew H; Mahmood, Sadiqa; Sanders, Dale
- Source
- 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) ICHI Healthcare Informatics (ICHI), 2021 IEEE 9th International Conference on. :258-264 Aug, 2021
- Subject
- Computing and Processing
COVID-19
Analytical models
Codes
Conferences
Medical services
Resource management
Informatics
Topic modelling
Latent Dirichlet Allocation
- Language
- ISSN
- 2575-2634
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.