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Abstract: TH-PO044

Machine Learning Approach for Hemodialysis Prescription: Model Development and Validation Study

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Authors

  • Bian, Xueqin, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
  • Ye, Hong, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
  • Zhou, Yang, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
  • Yang, Junwei, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
Background

The prediction model of hemodialysis prescription is established based on machine learning to achieve precise hemodialysis treatment.

Methods

We obtained 108,638 hemodialysis sessions in 965 independent maintenance hemodialysis(MHD)patients in our hospital from October 1, 2020 to June 31, 2021 using random sampling. The sessions were randomly divided into training (70%), calibration (10%), and testing (20%) sets. Apply XGBoost to analyze and extract effective feature data. XGBoost,RF, KNN, SVR, LR, MLP were used to develop the prescription model and model fusion.Training and update the model using reinforcement learning. The area under the receiver operating characteristic curves, the area under the precision-recall curves, F1 scores and MSE obtained to assess model stability and accuracy.

Results

There were 108,638 dialysis records in 965 patients, of whom 62.2 were male, the average age was 59.3±13.2 years, the median dialysis age was 102.3 months, BMI was 23.8±3.9, and the primary disease was diabetic nephropathy (38.6%). There are 13 labels in the hemodialysis program, of which 6 labels are continuous variables and the regression model is used, and 7 labels are discontinuous variables.The average accuracy is greater than 0.88, and the mean square error is less than 0.067.

Conclusion

The artificial intelligence dialysis prescription established based on machine learning has better stability and accuracy.

Funding

  • Government Support – Non-U.S.