Work

Construction and Explanation Analysis of a Hypotension Risk Prediction Model in Hemodialysis Based on Machine Learning

Year: 2024

Type: article

Abstract: Objective. To establish a risk prediction model for intradialytic hypotension (IDH) in maintenance hemodialysis (MHD) patients and to analyze the explainability of the risk prediction model. Methods. A total of 2,228,650 hemodialysis records of 1075 MHD patients were selected as the research objects. Thirteen important clinical features including demographic features and clinical features were screened, the blood pressure measured before hemodialysis was collected, then an IDH risk prediction model during hemodialysis was established based on a machine learning algorithm. The contribution of each feature to the risk prediction of IDH was measured based on the Gini evaluation index. The TreeSHAP method was used to provide global and individual explanations for the IDH risk prediction model. Results. Hemodialysis duration, pre-dialysis mean arterial pressure, and pre-dialysis systolic blood pressure were the most important predictive variables for the occurrence of IDH during hemodialysis in MHD patients. The best IDH risk prediction model based on machine learning had an accuracy of 0.92 (95% CI 0.90–0.94) and an AUC of 0.95 (95% CI 0.94–0.96), indicating that machine learning has a good effect on the prediction of IDH during hemodialysis treatment. Our research innovatively achieved IDH risk prediction during the entire hemodialysis period based on blood pressure before the start of hemodialysis and other clinical features, thus enabling the medical team to quickly adjust hemodialysis prescriptions or initiate treatment for timely management and prevention of IDH. Global and individual explanations of the IDH risk prediction model can help hemodialysis medical staff understand the overall prediction mechanism of the model, discover prediction outliers, and identify potential biases or errors in the model. Conclusions. The IDH risk prediction model has definite clinical value in actual hemodialysis treatment.

Source: Electronics

DOI: 10.3390/electronics13183773

Authors: Mingwei Zhang, Tianyi Zhang

Institution: University of Shanghai for Science and Technology

Language: English

Topic: Dialysis and Renal Disease Management

Subfield: Nephrology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,728