TY - GEN
T1 - Feature set for a prediction model of diabetic kidney disease progression
AU - Ono, Masaki
AU - Katsuki, Takayuki
AU - Makino, Masaki
AU - Haida, Kyoichi
AU - Suzuki, Atsushi
AU - Tokumasu, Reitaro
N1 - Publisher Copyright:
© 2020 European Federation for Medical Informatics (EFMI) and IOS Press.
PY - 2020/6/16
Y1 - 2020/6/16
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85086945889&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086945889&partnerID=8YFLogxK
U2 - 10.3233/SHTI200406
DO - 10.3233/SHTI200406
M3 - Conference contribution
C2 - 32570623
AN - SCOPUS:85086945889
T3 - Studies in Health Technology and Informatics
SP - 1289
EP - 1290
BT - Digital Personalized Health and Medicine - Proceedings of MIE 2020
A2 - Pape-Haugaard, Louise B.
A2 - Lovis, Christian
A2 - Madsen, Inge Cort
A2 - Weber, Patrick
A2 - Nielsen, Per Hostrup
A2 - Scott, Philip
PB - IOS Press
T2 - 30th Medical Informatics Europe Conference, MIE 2020
Y2 - 28 April 2020 through 1 May 2020
ER -