HES_icd10_example

A realistic sized simulated HES diagnoses with participant IDs and ICD-10 codes.

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_icd10_example function

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

Format

Example HES ICD-10 diagnoses — HES_icd10_example

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

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

<dt>age_diag</dt>
<dd>ICD-10 diagnosis age point (double).</dd>


</dl>

HES_icd10_example: Example HES ICD-10 diagnoses

Description

Usage

Arguments

Format

Examples