机器学习
特征选择
判别式
危险分层
医学
围手术期
人工智能
计算机科学
外科
内科学
作者
Ximing Nie,Jian Yang,Xinxin Li,Tianming Zhan,Dongdong Liu,Yan H,Yufei Wei,Xiran Liu,Jiaping Chen,Guoyi Gong,Zhengyu Wu,Zhi‐gang Yang,Wen Ma,Weibin Gu,Yuesong Pan,Yong Jiang,Xia Meng,Tao Liu,Jian Cheng,Zixiao Li,Zhongrong Miao,Liping Liu
标识
DOI:10.1136/svn-2023-002500
摘要
Background Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning method for predicting futile recanalisation. Methods Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management workflow. These models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following EVT. The optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation prediction. Results Using a hybrid feature selection approach, we trained and tested multiple classifiers on two independent patient cohorts (n=1122) to develop a hybrid machine learning-based prediction model. The model demonstrated superior discriminative ability compared with other models and scoring systems (area under the curve=0.80, 95% CI 0.73 to 0.87) and was transformed into a web application (RESCUE-FR Index) that provides a risk stratification system for individual prediction (accessible online at fr-index.biomind.cn/RESCUE-FR/). Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.
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