  Patient representation learning based on electronic health records (EHR) is a
critical task for disease prediction. This task aims to effectively extract
useful information on dynamic features. Although various existing works have
achieved remarkable progress, the model performance can be further improved by
fully extracting the trends, variations, and the correlation between the trends
and variations in dynamic features. In addition, sparse visit records limit the
performance of deep learning models. To address these issues, we propose the
Multi-perspective Patient Representation Extractor (MPRE) for disease
prediction. Specifically, we propose Frequency Transformation Module (FTM) to
extract the trend and variation information of dynamic features in the
time-frequency domain, which can enhance the feature representation. In the 2D
Multi-Extraction Network (2D MEN), we form the 2D temporal tensor based on
trend and variation. Then, the correlations between trend and variation are
captured by the proposed dilated operation. Moreover, we propose the
First-Order Difference Attention Mechanism (FODAM) to calculate the
contributions of differences in adjacent variations to the disease diagnosis
adaptively. To evaluate the performance of MPRE and baseline methods, we
conduct extensive experiments on two real-world public datasets. The experiment
results show that MPRE outperforms state-of-the-art baseline methods in terms
of AUROC and AUPRC.
