@inproceedings{7058fb2598894cfa8c71d1173f111578,
title = "Risk prediction of diabetic nephropathy via interpretable feature extraction from EHR using convolutional autoencoder",
abstract = "This paper describes a technology for predicting the aggravation of diabetic nephropathy from electronic health record (EHR). For the prediction, we used features extracted from event sequence of lab tests in EHR with a stacked convolutional autoencoder which can extract both local and global temporal information. The extracted features can be interpreted as similarities to a small number of typical sequences of lab tests, that may help us to understand the disease courses and to provide detailed health guidance. In our experiments on real-world EHRs, we confirmed that our approach performed better than baseline methods and that the extracted features were promising for understanding the disease.",
author = "Takayuki Katsuki and Masaki Ono and Akira Koseki and Michiharu Kudo and Kyoichi Haida and Jun Kuroda and Masaki Makino and Ryosuke Yanagiya and Atsushi Suzuki",
note = "Funding Information: T. Katsuki was supported in part by JST CREST Grant Number JPMJCR1304, Japan. Publisher Copyright: {\textcopyright} 2018 European Federation for Medical Informatics (EFMI) and IOS Press.; 40th Medical Informatics in Europe Conference, MIE 2018 ; Conference date: 24-04-2018 Through 26-04-2018",
year = "2018",
doi = "10.3233/978-1-61499-852-5-106",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "106--110",
editor = "Adrien Ugon and Daniel Karlsson and Klein, {Gunnar O.} and Anne Moen",
booktitle = "Building Continents of Knowledge in Oceans of Data",
address = "Netherlands",
}