COPER: Continuous Patient State Perceiver

TL;DR

COPER is a novel model combining neural ODEs and Perceiver architecture to effectively handle irregular time-series data in electronic health records, improving patient outcome predictions.

Contribution

The paper introduces COPER, a new approach integrating neural ODEs with Perceiver to model continuous patient states from irregular EHR data.

Findings

COPER outperforms baseline models in mortality prediction.

Neural ODEs enable effective handling of irregular time intervals.

The model demonstrates robustness across different irregularity levels.

Abstract

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.