  Electronic Health Records present a valuable modality for driving
personalized medicine, where treatment is tailored to fit individual-level
differences. For this purpose, many data-driven machine learning and
statistical models rely on the wealth of longitudinal EHRs to study patients'
physiological and treatment effects. However, longitudinal EHRs tend to be
sparse and highly missing, where missingness could also be informative and
reflect the underlying patient's health status. Therefore, the success of
data-driven models for personalized medicine highly depends on how the EHR data
is represented from physiological data, treatments, and the missing values in
the data. To this end, we propose a novel deep-learning model that learns the
underlying patient dynamics over time across multivariate data to generate
personalized realistic values conditioning on an individual's demographic
characteristics and treatments. Our proposed model, IGNITE (Individualized
GeNeration of Imputations in Time-series Electronic health records), utilises a
conditional dual-variational autoencoder augmented with dual-stage attention to
generate missing values for an individual. In IGNITE, we further propose a
novel individualized missingness mask (IMM), which helps our model generate
values based on the individual's observed data and missingness patterns. We
further extend the use of IGNITE from imputing missingness to a personalized
data synthesizer, where it generates missing EHRs that were never observed
prior or even generates new patients for various applications. We validate our
model on three large publicly available datasets and show that IGNITE
outperforms state-of-the-art approaches in missing data reconstruction and task
prediction.
