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 + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
AgeTopicModels results |
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