Abstract: SA-PO332
Machine Learning Approach for Hemodialysis Prescription: Model Development and Validation Study
Session Information
- Hemodialysis and Frequent Dialysis: Potpourri
November 05, 2022 | Location: Exhibit Hall, Orange County Convention Center‚ West Building
Abstract Time: 10:00 AM - 12:00 PM
Category: Dialysis
- 701 Dialysis: Hemodialysis and Frequent Dialysis
Authors
- Bian, Xueqin, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Zhou, Yang, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Ye, Hong, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Yang, Junwei, Second Affiliated Hospital of Nanjing Medical University, Nanjing, 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, Random Forest Regression, K-Nearest Neighbor, Support Vector Regression and Linear Regression 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 prescription, 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. The average compliance rate of dialysis prescriptions was 86%.
Conclusion
The artificial intelligence dialysis prescription established based on machine learning has better stability and accuracy.
Funding
- Government Support – Non-U.S.