CTMEG: A continuous-time medical event generation model for clinical prediction of long-term disease progression

Abstract

Long-term health monitoring indicates patient’s disease progression which is critical in improving the quality of patient life and physician’s decision-making. Predictive models based on Electronic Health Records (EHRs) can offer substantial clinical support by alerting subsequent disease-associated adverse events. Effective disease progression modeling involves two subtasks: 1) estimation of disease-associated event occurrence times 2) classification of occurred event types Recent time-aware disease predictive models, mainly based on recurrent neural networks or attention networks, specialize in future disease type prediction by accounting for the temporal irregularities in EHRs. This paper focuses on multi-step continuous-time disease prediction, which is more challenging as predictive models can easily fall into task conflicts between subtasks. We propose a multi-task disentangled Continuous-Time Medical Event Generation (CTMEG) model to simultaneously tackle the two subtasks. Unlike conventional continuous-time models, CTMEG encodes multi-view historical medical events and then simultaneously predicts multi-step disease types and occurrence times. First, a discrete Conditional Intensity Function (CIF) is designed to better estimate the disease occurrence time with limited available data. Second, to reduce task conflicts, a gated network is proposed to disentangle the rough patient representation into task-specific representations. Finally, we utilize a tailored CIF attention module to reduce error accumulation during the prediction process. Extensive experiments on the eICU and BFH databases demonstrate that the proposed CTMEG outperforms twelve competing models in long-term disease progression prediction. Our codes are available on github.²