  Evaluating the clinical similarities between pairwise patients is a
fundamental problem in healthcare informatics. A proper patient similarity
measure enables various downstream applications, such as cohort study and
treatment comparative effectiveness research. One major carrier for conducting
patient similarity research is Electronic Health Records(EHRs), which are
usually heterogeneous, longitudinal, and sparse. Though existing studies on
learning patient similarity from EHRs have shown being useful in solving real
clinical problems, their applicability is limited due to the lack of medical
interpretations. Moreover, most previous methods assume a vector-based
representation for patients, which typically requires aggregation of medical
events over a certain time period. As a consequence, temporal information will
be lost. In this paper, we propose a patient similarity evaluation framework
based on the temporal matching of longitudinal patient EHRs. Two efficient
methods are presented, unsupervised and supervised, both of which preserve the
temporal properties in EHRs. The supervised scheme takes a convolutional neural
network architecture and learns an optimal representation of patient clinical
records with medical concept embedding. The empirical results on real-world
clinical data demonstrate substantial improvement over the baselines. We make
our code and sample data available for further study.
