Feature set for a prediction model of diabetic kidney disease progression

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

doi:10.3233/SHTI200406

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 proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	Digital Personalized Health and Medicine - Proceedings of MIE 2020
Editors	Louise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
Publisher	IOS Press
Pages	1289-1290
Number of pages	2
ISBN (Electronic)	9781643680828
DOIs	https://doi.org/10.3233/SHTI200406
Publication status	Published - 16-06-2020
Externally published	Yes
Event	30th Medical Informatics Europe Conference, MIE 2020 - Geneva, Switzerland Duration: 28-04-2020 → 01-05-2020

Publication series

Name	Studies in Health Technology and Informatics
Volume	270
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	30th Medical Informatics Europe Conference, MIE 2020
Country	Switzerland
City	Geneva
Period	28-04-20 → 01-05-20

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/SHTI200406

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Ono, M., Katsuki, T., Makino, M., Haida, K., Suzuki, A., & Tokumasu, R. (2020). Feature set for a prediction model of diabetic kidney disease progression. In L. B. Pape-Haugaard, C. Lovis, I. C. Madsen, P. Weber, P. H. Nielsen, & P. Scott (Eds.), Digital Personalized Health and Medicine - Proceedings of MIE 2020 (pp. 1289-1290). (Studies in Health Technology and Informatics; Vol. 270). IOS Press. https://doi.org/10.3233/SHTI200406

Ono, Masaki ; Katsuki, Takayuki ; Makino, Masaki ; Haida, Kyoichi ; Suzuki, Atsushi ; Tokumasu, Reitaro. / Feature set for a prediction model of diabetic kidney disease progression. Digital Personalized Health and Medicine - Proceedings of MIE 2020. editor / Louise B. Pape-Haugaard ; Christian Lovis ; Inge Cort Madsen ; Patrick Weber ; Per Hostrup Nielsen ; Philip Scott. IOS Press, 2020. pp. 1289-1290 (Studies in Health Technology and Informatics).

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title = "Feature set for a prediction model of diabetic kidney disease progression",

abstract = "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.",

author = "Masaki Ono and Takayuki Katsuki and Masaki Makino and Kyoichi Haida and Atsushi Suzuki and Reitaro Tokumasu",

year = "2020",

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doi = "10.3233/SHTI200406",

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booktitle = "Digital Personalized Health and Medicine - Proceedings of MIE 2020",

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note = "30th Medical Informatics Europe Conference, MIE 2020 ; Conference date: 28-04-2020 Through 01-05-2020",

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Ono, M, Katsuki, T, Makino, M, Haida, K, Suzuki, A & Tokumasu, R 2020, Feature set for a prediction model of diabetic kidney disease progression. in LB Pape-Haugaard, C Lovis, IC Madsen, P Weber, PH Nielsen & P Scott (eds), Digital Personalized Health and Medicine - Proceedings of MIE 2020. Studies in Health Technology and Informatics, vol. 270, IOS Press, pp. 1289-1290, 30th Medical Informatics Europe Conference, MIE 2020, Geneva, Switzerland, 28-04-20. https://doi.org/10.3233/SHTI200406

Feature set for a prediction model of diabetic kidney disease progression. / Ono, Masaki; Katsuki, Takayuki; Makino, Masaki; Haida, Kyoichi; Suzuki, Atsushi; Tokumasu, Reitaro.

Digital Personalized Health and Medicine - Proceedings of MIE 2020. ed. / Louise B. Pape-Haugaard; Christian Lovis; Inge Cort Madsen; Patrick Weber; Per Hostrup Nielsen; Philip Scott. IOS Press, 2020. p. 1289-1290 (Studies in Health Technology and Informatics; Vol. 270).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Ono M, Katsuki T, Makino M, Haida K, Suzuki A, Tokumasu R. Feature set for a prediction model of diabetic kidney disease progression. In Pape-Haugaard LB, Lovis C, Madsen IC, Weber P, Nielsen PH, Scott P, editors, Digital Personalized Health and Medicine - Proceedings of MIE 2020. IOS Press. 2020. p. 1289-1290. (Studies in Health Technology and Informatics). https://doi.org/10.3233/SHTI200406