The graph convolutional network model effectively predicted 6-month readmission risk in patients with heart failure, achieving the highest area under the curve of 0.831 compared to other machine learning models.
Observational (n=1,948)
No
Does a graph convolutional network model accurately predict 6-month readmission risk in Chinese patients with heart failure?
A graph convolutional network model effectively predicts the 6-month risk of readmission in Chinese patients with heart failure, outperforming traditional machine learning algorithms.
Effect estimate: AUC 0.831 (95% CI 0.813-0.849)
p-value: p=<0.001
Background: Patients with heart failure frequently face the possibility of rehospitalization following an initial hospital stay, placing a significant burden on both patients and health care systems. Accurate predictive tools are crucial for guiding clinical decision-making and optimizing patient care. However, the effectiveness of existing models tailored specifically to the Chinese population is still limited. Objective: This study aimed to formulate a predictive model for assessing the likelihood of readmission among patients diagnosed with heart failure. Methods: In this study, we analyzed data from 1948 patients with heart failure in a hospital in Sichuan Province between 2016 and 2019. By applying 3 variable selection strategies, 29 relevant variables were identified. Subsequently, we constructed 6 predictive models using different algorithms: logistic regression, support vector machine, gradient boosting machine, Extreme Gradient Boosting, multilayer perception, and graph convolutional networks. Results: The graph convolutional network model showed the highest prediction accuracy with an area under the receiver operating characteristic curve of 0.831, accuracy of 75%, sensitivity of 52.12%, and specificity of 90.25%. Conclusions: The model crafted in this study proves its effectiveness in forecasting the likelihood of readmission among patients with heart failure, thus serving as a crucial reference for clinical decision-making.
Jiang et al. (Tue,) conducted a observational in Heart failure (n=1,948). Graph convolutional network (GCN) model vs. Traditional machine learning models (LR, SVM, GBM, XGBoost, MLP) was evaluated on 6-month hospital readmission prediction (AUC) (AUC 0.831, 95% CI 0.813-0.849, p=<0.001). The graph convolutional network model effectively predicted 6-month readmission risk in patients with heart failure, achieving the highest area under the curve of 0.831 compared to other machine learning models.
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