Cost prediction for ischemic heart disease hospitalization: Interpretable feature extraction using network analysis.
J Biomed Inform
; 154: 104652, 2024 Jun.
Article
en En
| MEDLINE
| ID: mdl-38718897
ABSTRACT
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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Isquemia Miocárdica
/
Hospitalización
Límite:
Humans
Idioma:
En
Revista:
J Biomed Inform
Asunto de la revista:
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article