diseasematrix2longdata

Disease matrix reformatting for ATM

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

diseasematrix2longdata function

<dl><dt>disease_matrix</dt>
<dd>a disease matrix with the first column name "eid", other column are disease names. Disease should be coded as 0,1.</dd></dl>

Arguments

Disease matrix reformatting for ATM — diseasematrix2longdata

<dl>

<dt>disease_matrix</dt>
<dd>a disease matrix with the first column name "eid", other column are disease names. Disease should be coded as 0,1.</dd>

</dl>

diseasematrix2longdata: Disease matrix reformatting for ATM

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