TY - GEN
T1 - Early nephrosis detection based on deep learning with clinical time-series data
AU - Yamasaki, Yohei
AU - Sugiyama, Osamu
AU - Hiragi, Shusuke
AU - Ohtera, Shosuke
AU - Yamamoto, Goshiro
AU - Sasaki, Hiroshi
AU - Okamoto, Kazuya
AU - Nambu, Masayuki
AU - Kuroda, Tomohiro
N1 - Publisher Copyright:
© 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
PY - 2019/8/21
Y1 - 2019/8/21
N2 - Nephrosis is disease characterized by abnormal protein loss from impaired kidney. We constructed early prediction model using machine learning from clinical time series data, that can predict onset of nephrosis for more than one month. Long short-term memory capable of recognizing temporal sequential data patterns, was adopted as early prediction model for nephrosis. We verified our proposed prediction model has higher accuracy compared with those of baseline classifiers by 5-fold cross validation.
AB - Nephrosis is disease characterized by abnormal protein loss from impaired kidney. We constructed early prediction model using machine learning from clinical time series data, that can predict onset of nephrosis for more than one month. Long short-term memory capable of recognizing temporal sequential data patterns, was adopted as early prediction model for nephrosis. We verified our proposed prediction model has higher accuracy compared with those of baseline classifiers by 5-fold cross validation.
UR - http://www.scopus.com/inward/record.url?scp=85071433303&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071433303&partnerID=8YFLogxK
U2 - 10.3233/SHTI190552
DO - 10.3233/SHTI190552
M3 - Conference contribution
C2 - 31438249
AN - SCOPUS:85071433303
T3 - Studies in Health Technology and Informatics
SP - 1596
EP - 1597
BT - MEDINFO 2019
A2 - Seroussi, Brigitte
A2 - Ohno-Machado, Lucila
A2 - Ohno-Machado, Lucila
A2 - Seroussi, Brigitte
PB - IOS Press
T2 - 17th World Congress on Medical and Health Informatics, MEDINFO 2019
Y2 - 25 August 2019 through 30 August 2019
ER -