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

doi:10.3233/978-1-61499-852-5-106

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

Department of Endocrinology and Metabolism

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

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.

Original language	English
Title of host publication	Building Continents of Knowledge in Oceans of Data
Subtitle of host publication	The Future of Co-Created eHealth - Proceedings of MIE 2018
Editors	Gunnar O. Klein, Daniel Karlsson, Anne Moen, Adrien Ugon
Publisher	IOS Press
Pages	106-110
Number of pages	5
ISBN (Electronic)	9781614998518
DOIs	https://doi.org/10.3233/978-1-61499-852-5-106
Publication status	Published - 01-01-2018
Event	40th Medical Informatics in Europe Conference, MIE 2018 - Gothenburg, Sweden Duration: 24-04-2018 → 26-04-2018

Publication series

Name	Studies in Health Technology and Informatics
Volume	247
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Other

Other	40th Medical Informatics in Europe Conference, MIE 2018
Country	Sweden
City	Gothenburg
Period	24-04-18 → 26-04-18

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All Science Journal Classification (ASJC) codes

Biomedical Engineering
Health Informatics
Health Information Management

Cite this

Katsuki, T., Ono, M., Koseki, A., Kudo, M., Haida, K., Kuroda, J., Makino, M., Yanagiya, R., & Suzuki, A. (2018). Risk prediction of diabetic nephropathy via interpretable feature extraction from EHR using convolutional autoencoder. In G. O. Klein, D. Karlsson, A. Moen, & A. Ugon (Eds.), Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018 (pp. 106-110). (Studies in Health Technology and Informatics; Vol. 247). IOS Press. https://doi.org/10.3233/978-1-61499-852-5-106

Katsuki, Takayuki ; Ono, Masaki ; Koseki, Akira ; Kudo, Michiharu ; Haida, Kyoichi ; Kuroda, Jun ; Makino, Masaki ; Yanagiya, Ryosuke ; Suzuki, Atsushi. / Risk prediction of diabetic nephropathy via interpretable feature extraction from EHR using convolutional autoencoder. Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. editor / Gunnar O. Klein ; Daniel Karlsson ; Anne Moen ; Adrien Ugon. IOS Press, 2018. pp. 106-110 (Studies in Health Technology and Informatics).

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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",

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language = "English",

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Katsuki, T, Ono, M, Koseki, A, Kudo, M, Haida, K, Kuroda, J, Makino, M, Yanagiya, R & Suzuki, A 2018, Risk prediction of diabetic nephropathy via interpretable feature extraction from EHR using convolutional autoencoder. in GO Klein, D Karlsson, A Moen & A Ugon (eds), Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. Studies in Health Technology and Informatics, vol. 247, IOS Press, pp. 106-110, 40th Medical Informatics in Europe Conference, MIE 2018, Gothenburg, Sweden, 24-04-18. https://doi.org/10.3233/978-1-61499-852-5-106

Risk prediction of diabetic nephropathy via interpretable feature extraction from EHR using convolutional autoencoder. / Katsuki, Takayuki; Ono, Masaki; Koseki, Akira; Kudo, Michiharu; Haida, Kyoichi; Kuroda, Jun; Makino, Masaki; Yanagiya, Ryosuke; Suzuki, Atsushi.

Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. ed. / Gunnar O. Klein; Daniel Karlsson; Anne Moen; Adrien Ugon. IOS Press, 2018. p. 106-110 (Studies in Health Technology and Informatics; Vol. 247).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Katsuki, Takayuki

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AB - 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.

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Katsuki T, Ono M, Koseki A, Kudo M, Haida K, Kuroda J et al. Risk prediction of diabetic nephropathy via interpretable feature extraction from EHR using convolutional autoencoder. In Klein GO, Karlsson D, Moen A, Ugon A, editors, Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018. IOS Press. 2018. p. 106-110. (Studies in Health Technology and Informatics). https://doi.org/10.3233/978-1-61499-852-5-106

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10.3233/978-1-61499-852-5-106