Risk prediction of diabetic nephropathy via interpretable feature extraction from EHR using convolutional autoencoder

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

研究成果: Conference contribution

11 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルBuilding Continents of Knowledge in Oceans of Data
ホスト出版物のサブタイトルThe Future of Co-Created eHealth - Proceedings of MIE 2018
編集者Adrien Ugon, Daniel Karlsson, Gunnar O. Klein, Anne Moen
出版社IOS Press BV
ページ106-110
ページ数5
ISBN(電子版)9781614998518
DOI
出版ステータスPublished - 2018
イベント40th Medical Informatics in Europe Conference, MIE 2018 - Gothenburg, Sweden
継続期間: 24-04-201826-04-2018

出版物シリーズ

名前Studies in Health Technology and Informatics
247
ISSN(印刷版)0926-9630
ISSN(電子版)1879-8365

Other

Other40th Medical Informatics in Europe Conference, MIE 2018
国/地域Sweden
CityGothenburg
Period24-04-1826-04-18

All Science Journal Classification (ASJC) codes

  • 生体医工学
  • 健康情報学
  • 健康情報管理

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引用スタイル