ArticleFrontiers in public health2026
Value of an automated machine learning model with post-hoc explanation for predicting healthcare-seeking delays among residents in Tibetan regions.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to investigate key determinants of healthcare-seeking delays among Tibetan residents and develop predictive models using automated machine learning (AutoML) with post-hoc SHAP interpretation alongside a clinical decision support system. Methods: Face-to-face surveys using structured questionnaires were administered to 1,879 Tibetan residents. Data processing employed an AutoML framework: datasets were partitioned into training ( Results: Among 1,879 participants, the healthcare-seeking delay incidence was 41.99%. The LightGBM model significantly outperformed conventional approaches (AUC > 0.86). SHAP feature importance analysis revealed the predictor hierarchy: Age > County hospital quality score > Distance to county hospital > Township health center quality score > Able to communicate in Chinese. Conclusion: A high-performance model with post-hoc SHAP interpretation accurately identifies geographical, cultural, and healthcare resource variables to accurately identify high-risk populations. The developed clinical decision support system enables risk computation through modular interfaces, providing an evidence-based tool for optimizing hierarchical diagnosis and resource allocation in Tibetan healthcare.
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