Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning

Masaki Makino, Ryo Yoshimoto, Masaki Ono, Toshinari Itoko, Takayuki Katsuki, Akira Koseki, Michiharu Kudo, Kyoichi Haida, Jun Kuroda, Ryosuke Yanagiya, Eiichi Saito, Kiyotaka Hoshinaga, Yukio Yuzawa, Atsushi Suzuki

研究成果: Article

抄録

Artificial intelligence (AI) is expected to support clinical judgement in medicine. We constructed a new predictive model for diabetic kidney diseases (DKD) using AI, processing natural language and longitudinal data with big data machine learning, based on the electronic medical records (EMR) of 64,059 diabetes patients. AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis. AI could predict DKD aggravation with 71% accuracy. Furthermore, the group with DKD aggravation had a significantly higher incidence of hemodialysis than the non-aggravation group, over 10 years (N = 2,900). The new predictive model by AI could detect progression of DKD and may contribute to more effective and accurate intervention to reduce hemodialysis.

元の言語English
記事番号11862
ジャーナルScientific reports
9
発行部数1
DOI
出版物ステータスPublished - 01-12-2019

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Artificial Intelligence
Diabetic Nephropathies
Renal Dialysis
Natural Language Processing
Electronic Health Records
Machine Learning
Logistic Models
Regression Analysis
Medicine
Incidence

All Science Journal Classification (ASJC) codes

  • General

これを引用

Makino, Masaki ; Yoshimoto, Ryo ; Ono, Masaki ; Itoko, Toshinari ; Katsuki, Takayuki ; Koseki, Akira ; Kudo, Michiharu ; Haida, Kyoichi ; Kuroda, Jun ; Yanagiya, Ryosuke ; Saito, Eiichi ; Hoshinaga, Kiyotaka ; Yuzawa, Yukio ; Suzuki, Atsushi. / Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. :: Scientific reports. 2019 ; 巻 9, 番号 1.
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abstract = "Artificial intelligence (AI) is expected to support clinical judgement in medicine. We constructed a new predictive model for diabetic kidney diseases (DKD) using AI, processing natural language and longitudinal data with big data machine learning, based on the electronic medical records (EMR) of 64,059 diabetes patients. AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis. AI could predict DKD aggravation with 71{\%} accuracy. Furthermore, the group with DKD aggravation had a significantly higher incidence of hemodialysis than the non-aggravation group, over 10 years (N = 2,900). The new predictive model by AI could detect progression of DKD and may contribute to more effective and accurate intervention to reduce hemodialysis.",
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Makino, M, Yoshimoto, R, Ono, M, Itoko, T, Katsuki, T, Koseki, A, Kudo, M, Haida, K, Kuroda, J, Yanagiya, R, Saito, E, Hoshinaga, K, Yuzawa, Y & Suzuki, A 2019, 'Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning', Scientific reports, 巻. 9, 番号 1, 11862. https://doi.org/10.1038/s41598-019-48263-5

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. / Makino, Masaki; Yoshimoto, Ryo; Ono, Masaki; Itoko, Toshinari; Katsuki, Takayuki; Koseki, Akira; Kudo, Michiharu; Haida, Kyoichi; Kuroda, Jun; Yanagiya, Ryosuke; Saito, Eiichi; Hoshinaga, Kiyotaka; Yuzawa, Yukio; Suzuki, Atsushi.

:: Scientific reports, 巻 9, 番号 1, 11862, 01.12.2019.

研究成果: Article

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AU - Kudo, Michiharu

AU - Haida, Kyoichi

AU - Kuroda, Jun

AU - Yanagiya, Ryosuke

AU - Saito, Eiichi

AU - Hoshinaga, Kiyotaka

AU - Yuzawa, Yukio

AU - Suzuki, Atsushi

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