Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms

Springer Science and Business Media LLC - Tập 45 - Trang 1521-1531 - 2021
ShiTeng Lin1,2, Yang Zou3, Jue Hu4, Lan Xiang5, LeHeng Guo5, XinPing Lin1,2, DaiZun Zou1,2, Xiaoping Gao5, Hui Liang5, JianJun Zou2,6, ZhiHong Zhao4, XiaoMing Dai7
1School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
2Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
3School of Pharmacy, University of Sydney, Sydney, Australia
4Department of Neurology, Changsha Central Hospital, Changsha, China
5Department of Neurology, The First Affiliated Hospital (People’s Hospital of Hunan Province), Hunan Normal University, Changsha, China
6Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
7Department of Hepatobiliary and Pancreas Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China

Tóm tắt

Intracranial aneurysms (IAs) remain a major public health concern and endovascular treatment (EVT) has become a major tool for managing IAs. However, the recurrence rate of IAs after EVT is relatively high, which may lead to the risk for aneurysm re-rupture and re-bleed. Thus, we aimed to develop and assess prediction models based on machine learning (ML) algorithms to predict recurrence risk among patients with IAs after EVT in 6 months. Patient population included patients with IAs after EVT between January 2016 and August 2019 in Hunan Provincial Peopleʼs Hospital, and an adaptive synthetic (ADASYN) sampling approach was applied for the entire imbalanced dataset. We developed five ML models and assessed the models. In addition, we used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. A total of 425 IAs were enrolled into this study, and 66 (15.5%) of which recurred in 6 months. Among the five ML models, gradient boosting decision tree (GBDT) model performed best. The area under curve (AUC) of the GBDT model on the testing set was 0.842 (sensitivity: 81.2%; specificity: 70.4%). Our study firstly demonstrated that ML-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after EVT in 6 months, and the GBDT model showed the optimal prediction performance.

Tài liệu tham khảo

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Springer Science and Business Media LLC

Tập 45

1521-1531

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