Feature Extraction from Electronic Health Records of Diabetic Nephropathy Patients with Convolutioinal Autoencoder

Takayuki Katsuki, Masaki Ono, Akira Koseki, Michiharu Kudo, Kyoichi Haida, Jun Kuroda, Masaki Makino, Ryosuke Yanagiya, Atsushi Suzuki

Abstract: This paper describes a feature extraction technology from event sequence of lab tests in electronic health record (EHR) for modeling diabetic nephropathy. We used a stacked convolutional autoencoder which can extract both local and global temporal information from the event sequence. The extracted features can be interpreted as similarities to a small number of typical sequences of lab tests. The extracted features in our prototyping experiment were promising for understanding of the long-term course of the disease.