Network Analysis-Driven Machine Learning Model for Identifying High-Cost Stroke Inpatients Using Hospital Discharge Data: Retrospective Study.

Background: The medical burden caused by stroke is increasingly severe, and a small minority of high-cost patients consume the majority of medical expenditures. Therefore, revealing the formation mechanisms of this population and exploring a scientific cost-risk stratification system are crucial for improving the quality of care and achieving the optimal allocation of medical resources. Objective: This study aimed to construct a comorbidity network for patients with stroke using standardized front-page medical record data, extract network features that reflect complex disease interactions, and develop identification models in combination with machine learning algorithms. The study focused on building a core model integrating variables from the near-discharge stage for stratifying the risk of high hospitalization costs in patients at the near-discharge stage. In addition, an early prediction model was developed using only data available at admission. Methods: We conducted a retrospective study, collecting the hospital discharge data of inpatients with stroke from a tertiary hospital in Northeast China between 2021 and 2023. The data from 2021 to 2022 were used to construct a network and extract features to capture the potential relationship between diseases and high costs. Using the 2023 data partitioned into training and testing sets, we developed 5 models to identify inpatients with stroke who incurred high hospitalization costs and compared their performance when input with different features. In addition, the Shapley Additive Explanations interpretability method was adopted to explain the global and local contributions of the model features. Results: The inclusion of network features significantly improved the model's performance, among which Extreme Gradient Boosting performed the best. The global feature importance showed that network features occupied a major proportion. The results of the Shapley Additive Explanations interaction analysis indicated potential phased changes in patient resource consumption. However, the overall performance of the early identification model constructed solely from admission data was subject to clear limitations. Conclusions: This study developed an integrated framework combining comorbidity network analysis with machine learning, which significantly improved the accuracy of identifying inpatients with stroke at high risk of incurring excessive hospitalization costs. The core model demonstrated good performance in risk stratification during the near-discharge stage, showing potential for application in the formulation of risk management strategies and the optimization of health care resource allocation. It also laid the foundation for the subsequent development of more accurate early identification models.