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 | |
| 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
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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
TY - GEN
T1 - Risk prediction of diabetic nephropathy via interpretable feature extraction from EHR using convolutional autoencoder
AU - Katsuki, Takayuki
AU - Ono, Masaki
AU - Koseki, Akira
AU - Kudo, Michiharu
AU - Haida, Kyoichi
AU - Kuroda, Jun
AU - Makino, Masaki
AU - Yanagiya, Ryosuke
AU - Suzuki, Atsushi
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85046554535&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046554535&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-852-5-106
DO - 10.3233/978-1-61499-852-5-106
M3 - Conference contribution
C2 - 29677932
AN - SCOPUS:85046554535
T3 - Studies in Health Technology and Informatics
SP - 106
EP - 110
BT - Building Continents of Knowledge in Oceans of Data
A2 - Klein, Gunnar O.
A2 - Karlsson, Daniel
A2 - Moen, Anne
A2 - Ugon, Adrien
PB - IOS Press
ER -