부산대학교 도서관

Recent methods of patient clinical outcome prediction focus on embedding the temporal time-series data by sequential data encoders without considering the dependency between the different variables and the static demographics data. To solve this problem and achieve better patient outcome prediction, we propose an attention-based memory fusion (AMF) network with Gated Recurrent Unit (GRU) (called GRU-AMFN) to model the dependency between the different time-series and static demographic data and extract effective personalized representation about the patient’s clinical health status. We evaluate our proposed GRU-AMFN method on eICU, a publicly available dataset, to validate its effectiveness for the in-hospital mortality prediction task. Experimental results demonstrate that our proposed method outperforms several state-of-the-art models for the in-hospital mortality prediction task. Ablation studies show the effectiveness of the proposed attention-based memory fusion module and the adaptive fusion module. Besides, our proposed method finds several static demographic and time-series features that are important for mortality prediction.