HES_age_example

A realistic sized simulated Hospital Episode Statistics (HES) data
with participant IDs and ages at diagnosis, used in examples and tests.
You would expect the run time of AgeTopicModels on these data is similar to what you face in real life

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

HES_age_example function

A data frame/tibble with example rows. Typical columns include:<dl>
<dt>eid</dt>
<dd>Participant identifier (integer or character).</dd><dt>age_diag</dt>
<dd>Age at diagnosis (numeric).</dd><dt>diag_icd10</dt>
<dd>ICD-10 diagnosis code (character).</dd>
</dl>

Format

Example HES diagnosis ages — HES_age_example

A data frame/tibble with example rows. Typical columns include:<dl>
<dt>eid</dt>
<dd>Participant identifier (integer or character).</dd>

<dt>age_diag</dt>
<dd>Age at diagnosis (numeric).</dd>

<dt>diag_icd10</dt>
<dd>ICD-10 diagnosis code (character).</dd>


</dl>

HES_age_example: Example HES diagnosis ages

Description

Usage

Arguments

Format

Examples