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

doi:10.3233/SHTI190552

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

研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

1 被引用数 (Scopus)

抄録

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.

本文言語	英語
ホスト出版物のタイトル	MEDINFO 2019
ホスト出版物のサブタイトル	Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
編集者	Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
出版社	IOS Press
ページ	1596-1597
ページ数	2
ISBN（電子版）	9781643680026
DOI	https://doi.org/10.3233/SHTI190552
出版ステータス	出版済み - 21-08-2019
外部発表	はい
イベント	17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, フランス継続期間: 25-08-2019 → 30-08-2019

出版物シリーズ

名前	Studies in Health Technology and Informatics
巻	264
ISSN（印刷版）	0926-9630
ISSN（電子版）	1879-8365

会議

会議	17th World Congress on Medical and Health Informatics, MEDINFO 2019
国/地域	フランス
City	Lyon
Period	25-08-19 → 30-08-19

All Science Journal Classification (ASJC) codes

生体医工学
健康情報学
健康情報管理

文献へのアクセス

10.3233/SHTI190552

他のファイルとリンク

引用スタイル

Yamasaki, Y., Sugiyama, O., Hiragi, S., Ohtera, S., Yamamoto, G., Sasaki, H., Okamoto, K., Nambu, M., & Kuroda, T. (2019). Early nephrosis detection based on deep learning with clinical time-series data. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 1596-1597). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190552

Yamasaki, Yohei ; Sugiyama, Osamu ; Hiragi, Shusuke その他. / Early nephrosis detection based on deep learning with clinical time-series data. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. editor / Brigitte Seroussi ; Lucila Ohno-Machado ; Lucila Ohno-Machado ; Brigitte Seroussi. IOS Press, 2019. pp. 1596-1597 (Studies in Health Technology and Informatics).

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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.",

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

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Yamasaki, Y, Sugiyama, O, Hiragi, S, Ohtera, S, Yamamoto, G, Sasaki, H, Okamoto, K, Nambu, M & Kuroda, T 2019, Early nephrosis detection based on deep learning with clinical time-series data. in B Seroussi, L Ohno-Machado, L Ohno-Machado & B Seroussi (eds), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 264, IOS Press, pp. 1596-1597, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, フランス, 25-08-19. https://doi.org/10.3233/SHTI190552

Early nephrosis detection based on deep learning with clinical time-series data. / Yamasaki, Yohei; Sugiyama, Osamu; Hiragi, Shusuke その他.
MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. ed. / Brigitte Seroussi; Lucila Ohno-Machado; Lucila Ohno-Machado; Brigitte Seroussi. IOS Press, 2019. p. 1596-1597 (Studies in Health Technology and Informatics; Vol. 264).

研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

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.

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PB - IOS Press

T2 - 17th World Congress on Medical and Health Informatics, MEDINFO 2019

Y2 - 25 August 2019 through 30 August 2019

ER -

Yamasaki Y, Sugiyama O, Hiragi S, Ohtera S, Yamamoto G, Sasaki H その他. Early nephrosis detection based on deep learning with clinical time-series data. In Seroussi B, Ohno-Machado L, Ohno-Machado L, Seroussi B, editors, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. IOS Press. 2019. p. 1596-1597. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI190552

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

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出版物シリーズ

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All Science Journal Classification (ASJC) codes

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