MedGAITS: a graph autoencoder network for modeling irregular time series data in electronic medical records

Yueying Wang, Shan Jiang, Chuyue Wang, Zeyu Wan, Yusi Fan, Kewei Li, Lan Huang, Fengfeng Zhou

Abstract: The widespread adoption of electronic medical records (EMR) has facilitated the prediction of patient prognosis and disease progression, yet inherent issues such as irregular sampling and missing values continue to pose challenges for clinical time-series analysis. This study aims to develop a robust framework capable of effectively handling incomplete EMR data while capturing complex temporal patterns and feature interaction.