UKB_349_disease

A character vector or table listing the set of disease phenotypes used
in examples/vignettes.

datasets

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>.

Xilin Jiang

AgeTopicModels

Inferring Age-Dependent Disease Topic from Diagnosis Data

UKB_349_disease function

A data frame/tibble containing disease identifiers/names. Columns include:<dl>
<dt>diag_icd10</dt>
<dd>Phecode (character).</dd><dt>occ</dt>
<dd>number of distinct patient in UKB</dd>
</dl>@examples
head(UKB_349_disease)

Format

List of 349 UK Biobank diseases (example) — UKB_349_disease

A data frame/tibble containing disease identifiers/names. Columns include:<dl>
<dt>diag_icd10</dt>
<dd>Phecode (character).</dd>

<dt>occ</dt>
<dd>number of distinct patient in UKB</dd>


</dl>@examples
head(UKB_349_disease)

UKB_349_disease: List of 349 UK Biobank diseases (example)

Description

Usage

Arguments

Format