Feature set for a prediction model of diabetic kidney disease progression

Masaki Ono, Takayuki Katsuki, Masaki Makino, Kyoichi Haida, Atsushi Suzuki, Reitaro Tokumasu

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

1 Citation (Scopus)


In this paper, we propose feature extraction method for prediction model for at the early stage of diabetic kidney disease (DKD) progression. DKD needs continuous treatment; however, a hospital visit interval of a patient at the early stage of DKD is normally from one month to three months, and this is not a short time period. Therefore it makes difficult to apply sophisticated approaches such as using convolutional neural networks because of the data limitation. The propose method uses with hierarchical clustering that can estimate a suitable interval for grouping inputted sequences. We evaluate the proposed method with a real-EMR dataset that consists of 30,810 patient records and conclude that the proposed method outperforms the baseline methods derived from related work.

Original languageEnglish
Title of host publicationDigital Personalized Health and Medicine - Proceedings of MIE 2020
EditorsLouise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
PublisherIOS Press
Number of pages2
ISBN (Electronic)9781643680828
Publication statusPublished - 16-06-2020
Externally publishedYes
Event30th Medical Informatics Europe Conference, MIE 2020 - Geneva, Switzerland
Duration: 28-04-202001-05-2020

Publication series

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


Conference30th Medical Informatics Europe Conference, MIE 2020

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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