phecode_icd10cm

Mapping table between ICD-10-CM codes and PheCodes.

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

phecode_icd10cm function

A data frame/tibble. Common columns include:<dl>
<dt>ICD10</dt>
<dd>ICD-10-CM code (character).</dd><dt>phecode</dt>
<dd>PheCode (character).</dd><dt>exclude_range</dt>
<dd>ancestor PheCode range (character).</dd><dt>exclude_name</dt>
<dd>ancestor PheCode name (character).</dd>
</dl>

Format

ICD-10-CM &lt;-&gt; PheCode mapping — phecode_icd10cm

A data frame/tibble. Common columns include:<dl>
<dt>ICD10</dt>
<dd>ICD-10-CM code (character).</dd>

<dt>phecode</dt>
<dd>PheCode (character).</dd>

<dt>exclude_range</dt>
<dd>ancestor PheCode range (character).</dd>

<dt>exclude_name</dt>
<dd>ancestor PheCode name (character).</dd>


</dl>

phecode_icd10cm: ICD-10-CM <-> PheCode mapping

Description

Usage

Arguments

Format

Examples