Cost prediction for ischemic heart disease hospitalization: Interpretable feature extraction using network analysis.

OBJECTIVES: Ischemic heart disease (IHD) is a significant contributor to global mortality and disability, imposing a substantial social and economic burden on individuals and healthcare systems. To enhance the efficient allocation of medical resources and ultimately benefit a larger population, accurate prediction of healthcare costs is crucial.

METHODS: We developed an interpretable IHD hospitalization cost prediction model that integrates network analysis with machine learning. Specifically, our network-enhanced model extracts explainable features by leveraging a diagnosis-procedure concurrence network and advanced graph kernel techniques, facilitating the capture of intricate relationships between medical codes.

RESULTS: The proposed model achieved an R2 of 0.804 ± 0.008 and a root mean square error (RMSE) of 17,076 ± 420 CNY on the temporal validation dataset, demonstrating comparable performance to the model employing less interpretable code embedding features (R2 : 0.800 ± 0.008; RMSE: 17,279 ± 437 CNY) and the hybrid graph isomorphism network (R2 : 0.802 ± 0.007; RMSE: 17,249 ± 387 CNY). The interpretation of the network-enhanced model assisted in pinpointing specific diagnoses and procedures associated with higher hospitalization costs, including acute kidney injury, permanent atrial fibrillation, intra-aortic balloon bump, and temporary pacemaker placement, among others.

CONCLUSION: Our analysis results demonstrate that the proposed model strikes a balance between predictive accuracy and interpretability. It aids in identifying specific diagnoses and procedures associated with higher hospitalization costs, underscoring its potential to support intelligent management of IHD.