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

Scientific Reports
Masaki MakinoAtsushi Suzuki

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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Citations

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Related Concepts

Diabetes
Glycosuria, Renal
Hemodialysis
Intelligence
Judgment
Learning
Medicine
Logistic Regression
Disease Progression
Electronic Health Records

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