longdata2diseasematrix

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

longdata2diseasematrix function

<dl><dt>rec_data</dt>
<dd>A diagnosis data frame with three columns; format data as HES_age_example; first column is individual ids (eid), second column is the disease code (diag_icd10);
third column is the age at diagnosis (age_diag). Note for each individual, we only keep the first onset of each diseases. Therefore, if there are multiple incidences of the same disease
within each individual, the rest will be ignored.</dd></dl>

Arguments

Title — longdata2diseasematrix

<dl>

<dt>rec_data</dt>
<dd>A diagnosis data frame with three columns; format data as HES_age_example; first column is individual ids (eid), second column is the disease code (diag_icd10);
third column is the age at diagnosis (age_diag). Note for each individual, we only keep the first onset of each diseases. Therefore, if there are multiple incidences of the same disease
within each individual, the rest will be ignored.</dd>

</dl>

longdata2diseasematrix: Title

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