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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MDPI AG
2025-06-01
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| Series: | Mathematics |
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| author | Yue Zhang Yuantao Xie |
| author_facet | Yue Zhang Yuantao Xie |
| author_sort | Yue Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f253d0f3ca8a4e19b74c48b5cf657241 |
| institution | Matheson Library |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-f253d0f3ca8a4e19b74c48b5cf6572412025-07-11T14:40:35ZengMDPI AGMathematics2227-73902025-06-011313213910.3390/math13132139Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification ApproachesYue Zhang0Yuantao Xie1School of Insurance, University of International Business and Economics, No. 10 Huixin East Street, Chaoyang District, Beijing 100029, ChinaSchool of Insurance, University of International Business and Economics, No. 10 Huixin East Street, Chaoyang District, Beijing 100029, ChinaTranscatheter 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.https://www.mdpi.com/2227-7390/13/13/2139TAVR surgery complicationsimbalanced datamulti-label classification |
| spellingShingle | Yue Zhang Yuantao Xie Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches Mathematics TAVR surgery complications imbalanced data multi-label classification |
| title | Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches |
| title_full | Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches |
| title_fullStr | Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches |
| title_full_unstemmed | Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches |
| title_short | Risk Measurement of TAVR Surgical Complications Based on Unbalanced Multilabel Classification Approaches |
| title_sort | risk measurement of tavr surgical complications based on unbalanced multilabel classification approaches |
| topic | TAVR surgery complications imbalanced data multi-label classification |
| url | https://www.mdpi.com/2227-7390/13/13/2139 |
| work_keys_str_mv | AT yuezhang riskmeasurementoftavrsurgicalcomplicationsbasedonunbalancedmultilabelclassificationapproaches AT yuantaoxie riskmeasurementoftavrsurgicalcomplicationsbasedonunbalancedmultilabelclassificationapproaches |