age_imputation

imputing missing age if you can't find some of them
The function does two stage imputation:
i. if the individual has other age label -- use the mean, min, or max of other age labels for the missing ones.
ii. if the individual has no age label -- use the mean, min, max for all the diagnosis codes
iii. if there is no age info available for any of this code, we will impute it as the mean of all age codes in the data

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

age_imputation function

<dl><dt>rec_data_missing_age</dt>
<dd>a data frame with missing age info</dd>
<dt>method</dt>
<dd>use one of the three choices "mean", "min", "max"</dd></dl>

Arguments

imputing missing age if you can't find some of them
The function does two stage imputation:
i. if the individual has other age label -- use the mean, min, or max of other age labels for the missing ones.
ii. if the individual has no age label -- use the mean, min, max for all the diagnosis codes
iii. if there is no age info available for any of this code, we will impute it as the mean of all age codes in the data — age_imputation

<dl>

<dt>rec_data_missing_age</dt>
<dd>a data frame with missing age info</dd>


<dt>method</dt>
<dd>use one of the three choices "mean", "min", "max"</dd>

</dl>

imputing missing age if you can't find some of them
The function does two stage imputation:
i. if the individual has other age label -- use the mean, min, or max of other age labels for the missing ones.
ii. if the individual has no age label -- use the mean, min, max for all the diagnosis codes
iii. if there is no age info available for any of this code, we will impute it as the mean of all age codes in the data

Description

Usage

Value

Arguments

Examples