Early nephrosis detection based on deep learning with clinical time-series data

Yohei Yamasaki, Osamu Sugiyama, Shusuke Hiragi, Shosuke Ohtera, Goshiro Yamamoto, Hiroshi Sasaki, Kazuya Okamoto, Masayuki Nambu, Tomohiro Kuroda

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

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Pages1596-1597
Number of pages2
ISBN (Electronic)9781643680026
DOIs
Publication statusPublished - 21-08-2019
Externally publishedYes
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: 25-08-201930-08-2019

Publication series

NameStudies in Health Technology and Informatics
Volume264
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019
Country/TerritoryFrance
CityLyon
Period25-08-1930-08-19

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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