  Machine learning for data-driven diagnosis has been actively studied in
medicine to provide better healthcare. Supporting analysis of a patient cohort
similar to a patient under treatment is a key task for clinicians to make
decisions with high confidence. However, such analysis is not straightforward
due to the characteristics of medical records: high dimensionality,
irregularity in time, and sparsity. To address this challenge, we introduce a
method for similarity calculation of medical records. Our method employs event
and sequence embeddings. While we use an autoencoder for the event embedding,
we apply its variant with the self-attention mechanism for the sequence
embedding. Moreover, in order to better handle the irregularity of data, we
enhance the self-attention mechanism with consideration of different time
intervals. We have developed a visual analytics system to support comparative
studies of patient records. To make a comparison of sequences with different
lengths easier, our system incorporates a sequence alignment method. Through
its interactive interface, the user can quickly identify patients of interest
and conveniently review both the temporal and multivariate aspects of the
patient records. We demonstrate the effectiveness of our design and system with
case studies using a real-world dataset from the neonatal intensive care unit
of UC Davis.
