Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches
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 li...
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| Những tác giả chính: | , |
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| Định dạng: | Bài viết |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
MDPI AG
2025-06-01
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| Loạt: | Mathematics |
| Những chủ đề: | |
| Truy cập trực tuyến: | https://www.mdpi.com/2227-7390/13/13/2139 |
| Các nhãn: |
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| Tóm tắt: | 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. |
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| số ISSN: | 2227-7390 |