AgeTopicModels: Inferring Age-Dependent Disease Topic from Diagnosis Data
We propose an age-dependent topic modelling (ATM) model, providing a low-rank representation of longitudinal records of hundreds of distinct diseases in large electronic health record data sets. The model assigns to each individual topic weights for several disease topics; each disease topic reflects a set of diseases that tend to co-occur as a function of age, quantified by age-dependent topic loadings for each disease. The model assumes that for each disease diagnosis, a topic is sampled based on the individual’s topic weights (which sum to 1 across topics, for a given individual), and a disease is sampled based on the individual’s age and the age-dependent topic loadings (which sum to 1 across diseases, for a given topic at a given age). The model generalises the Latent Dirichlet Allocation (LDA) model by allowing topic loadings for each topic to vary with age. References: Jiang (2023) <doi:10.1038/s41588-023-01522-8>.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.5) |
| Imports: | dplyr,ggplot2,ggrepel, grDevices,gtools,magrittr,pROC,reshape2,rlang, stats,stringr,tibble,tidyr, utils |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2025-10-21 |
| DOI: | 10.32614/CRAN.package.AgeTopicModels |
| Author: | Xilin Jiang [aut, cre] |
| Maintainer: | Xilin Jiang <jiangxilin1 at gmail.com> |
| License: | MIT + fileLICENSE |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | AgeTopicModels results |
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