loading2weights

Mapping individuals to fixed topic loadings.

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

loading2weights function

<dl><dt>data</dt>
<dd>the set of diseases, formatted same way as HES_age_example</dd>
<dt>ds_list</dt>
<dd>a list of diseases that correspond to the topic loadings that patients are mapped to
formatted as UKB_349_disease; default is set to be UKB_349_disease.</dd>
<dt>topics</dt>
<dd>The topics that are used to map patients. Default is set to be UKB_HES_10topics,
which are the inferred topics from 349 Phecodes from the UK Biobank HES data.
Details of these topics are available in the paper "Age-dependent topic modelling of
comorbidities in UK Biobank identifies disease subtypes with differential genetic risk".</dd></dl>

Arguments

Mapping individuals to fixed topic loadings. — loading2weights

<dl>

<dt>data</dt>
<dd>the set of diseases, formatted same way as HES_age_example</dd>


<dt>ds_list</dt>
<dd>a list of diseases that correspond to the topic loadings that patients are mapped to
formatted as UKB_349_disease; default is set to be UKB_349_disease.</dd>


<dt>topics</dt>
<dd>The topics that are used to map patients. Default is set to be UKB_HES_10topics,
which are the inferred topics from 349 Phecodes from the UK Biobank HES data.
Details of these topics are available in the paper "Age-dependent topic modelling of
comorbidities in UK Biobank identifies disease subtypes with differential genetic risk".</dd>

</dl>

loading2weights: Mapping individuals to fixed topic loadings.

Description

Usage

Value

Arguments

Examples