  In electronic health records (EHRs), irregular time-series (ITS) occur
naturally due to patient health dynamics, reflected by irregular hospital
visits, diseases/conditions and the necessity to measure different vitals signs
at each visit etc. ITS present challenges in training machine learning
algorithms which mostly are built on assumption of coherent fixed dimensional
feature space. In this paper, we propose a novel COntinuous patient state
PERceiver model, called COPER, to cope with ITS in EHRs. COPER uses Perceiver
model and the concept of neural ordinary differential equations (ODEs) to learn
the continuous time dynamics of patient state, i.e., continuity of input space
and continuity of output space. The neural ODEs help COPER to generate regular
time-series to feed to Perceiver model which has the capability to handle
multi-modality large-scale inputs. To evaluate the performance of the proposed
model, we use in-hospital mortality prediction task on MIMIC-III dataset and
carefully design experiments to study irregularity. The results are compared
with the baselines which prove the efficacy of the proposed model.
