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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publicationBuilding Continents of Knowledge in Oceans of Data
Subtitle of host publicationThe Future of Co-Created eHealth - Proceedings of MIE 2018
EditorsGunnar O. Klein, Daniel Karlsson, Anne Moen, Adrien Ugon
PublisherIOS Press
Pages106-110
Number of pages5
ISBN (Electronic)9781614998518
DOIs
Publication statusPublished - 01-01-2018
Event40th Medical Informatics in Europe Conference, MIE 2018 - Gothenburg, Sweden
Duration: 24-04-201826-04-2018

Publication series

NameStudies in Health Technology and Informatics
Volume247
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other40th Medical Informatics in Europe Conference, MIE 2018
CountrySweden
CityGothenburg
Period24-04-1826-04-18

Fingerprint

Electronic Health Records
Diabetic Nephropathies
Feature extraction
Health
Technology
Experiments

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., ... 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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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.",
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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 proceedingConference 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