COPER: Continuous Patient State Perceiver
This work addresses a domain-specific problem for healthcare analytics by handling irregular data in EHRs, though it appears incremental as it builds on existing methods like Perceiver and neural ODEs.
The authors tackled the challenge of irregular time-series in electronic health records by proposing COPER, a model that combines Perceiver and neural ODEs to learn continuous patient dynamics, achieving improved performance on in-hospital mortality prediction using the MIMIC-III dataset.
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.