Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches

Yue Zhang and Yuantao Xie ()
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Yue Zhang: School of Insurance, University of International Business and Economics, No. 10 Huixin East Street, Chaoyang District, Beijing 100029, China
Yuantao Xie: School of Insurance, University of International Business and Economics, No. 10 Huixin East Street, Chaoyang District, Beijing 100029, China

Mathematics, 2025, vol. 13, issue 13, 1-18

Abstract: Transcatheter aortic valve replacement (TAVR) is a high-risk cardiovascular interventional procedure with a high incidence of postoperative complications, urgently requiring more refined risk identification and mitigation strategies. The main challenges in assessing the risk of TAVR complications lie in the scarcity of real-world data and the co-occurrence of multiple complications. This study developed an adjustment evaluation model that adapts randomised clinical trial (RCT) evidence to real-world data (RWD) and adopted multi-label classification methods that incorporate a LocalGLMnet-like regularization term, enabling data-adaptive parameter shrinkage for more accurate estimation. In the empirical analysis, with real surgical data from a hospital in the United States, a combination of multi-label random sampling and representative multi-label classification algorithms was used to fit the data. The model was compared across multiple evaluation metrics, including Hamming loss, ranking loss, and micro-AUC, to ensure robust results. The model used in this paper bridges the gap between medical risk prediction and insurance actuarial science, provides a practical data modelling foundation and algorithmic support for the future development of post-operative complication insurance products that precisely align with clinical risk.

Keywords: TAVR surgery complications; imbalanced data; multi-label classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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