Artificial Intelligence Predicts Progress of Diabetic Kidney Disease-Novel Prediction Model Construction with Big Data Machine Learning

Background: Diabetic kidney diseases (DKD) including diabetic nephropathy is the most frequent cause of hemodialysis (HD), and more precise prediction model could be useful to early intervention of DKD. Methods: We constructed new prediction model for DKD by using artificial intelligence (AI) based on electronic medical records (EMRs). From EMRs of 64,059 diabetes patients who visited our hospital, we extracted a variety of features. This model uses the stage of nephropathy as labels, and predicts whether the stage 1 patients will move up their stage after 180 days. Results: AI constructed new prediction model by big data machine learning. First, AI extracted raw features in past 6 months at reference period, and selected 22 factors. Then, time series data analysis using convolutional autoencoder was conducted to find time series patterns relating to 6-month DKD aggravation. AI then constructed the prediction model with 17raw features as well as time series and text as secondary features using logistic regression. Finally, AI predicted DKD aggravation with 0.74 AUC score at maximum. Furthermore, DKD aggravation group had significantly higher incidence of HD than non-aggravation group in 10 years. Conclusion: The new prediction model by AI could detect progress of DKD, which could contribute to more effective and accurate intervention to reduce HD.