plot_lfa_topics

Title plot topic loadings for LFA.

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

plot_lfa_topics function

<dl><dt>disease_names</dt>
<dd>the list of disease names, ordered as the topic.</dd>
<dt>beta</dt>
<dd>disease topics, which should be a matrix of K-by-disease.</dd>
<dt>plot_title</dt>
<dd>the title of the figure.</dd></dl>

Arguments

Title plot topic loadings for LFA. — plot_lfa_topics

<dl>

<dt>disease_names</dt>
<dd>the list of disease names, ordered as the topic.</dd>


<dt>beta</dt>
<dd>disease topics, which should be a matrix of K-by-disease.</dd>


<dt>plot_title</dt>
<dd>the title of the figure.</dd>

</dl>

plot_lfa_topics: Title plot topic loadings for LFA.

Description

Usage

Value

Arguments

Examples