  Electronic health records (EHRs) are usually highly dimensional,
heterogeneous, and multimodal. Besides, the random recording of clinical
variables results in high missing rates and uneven time intervals between
adjacent records in the multivariate clinical time-series data extracted from
EHRs. Current works using clinical time-series data for patient representation
regard the patients' physiological status as a discrete process described by
sporadically collected records. However, changes in the patient's physiological
condition are continuous and dynamic processes. The perception of time and
velocity of change is crucial for patient representation learning. In this
study, we propose a time- and velocity-aware gated recurrent unit model
(GRU-TV) for patient representation learning of clinical multivariate
time-series data in a time-continuous manner. The neural ordinary differential
equations (ODEs) and velocity perception mechanism are applied to perceive the
time interval between adjacent records and changing rate of the patient's
physiological status, respectively. Our experiments on two real clinical EHR
datasets (PhysioNet2012, MIMIC-III) establish that GRU-TV is a robust model on
computer-aided diagnosis (CAD) tasks, especially on sequences with
high-variance time intervals.
