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Electronic health records (EHRs) are usually highly dimensional,heterogeneous, and multimodal. Besides, the random recording of clinicalvariables results in high missing rates and uneven time intervals betweenadjacent records in the multivariate clinical time-series data extracted fromEHRs. Current works using clinical time-series data for patient representationregard the patients' physiological status as a discrete process described bysporadically collected records. However, changes in the patient's physiologicalcondition are continuous and dynamic processes. The perception of time andvelocity of change is crucial for patient representation learning. In thisstudy, we propose a time- and velocity-aware gated recurrent unit model(GRU-TV) for patient representation learning of clinical multivariatetime-series data in a time-continuous manner. The neural ordinary differentialequations (ODEs) and velocity perception mechanism are applied to perceive thetime interval between adjacent records and changing rate of the patient'sphysiological status, respectively. Our experiments on two real clinical EHRdatasets (PhysioNet2012, MIMIC-III) establish that GRU-TV is a robust model oncomputer-aided diagnosis (CAD) tasks, especially on sequences withhigh-variance time intervals.

GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data