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

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)


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.

Original languageEnglish
Article number11862
JournalScientific reports
Issue number1
Publication statusPublished - 01-12-2019

All Science Journal Classification (ASJC) codes

  • General


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