Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications. Comment on Br J Anaesth 2024; 132: 1315–26

IF 9.1 1区医学 Q1 ANESTHESIOLOGY

British journal of anaesthesia Pub Date : 2024-07-15 DOI:10.1016/j.bja.2024.05.035

引用次数: 0

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利用术中呼吸动态特征开发并验证术后肺部并发症的可解释机器学习模型评论 Br J Anaesth 2024; 132: 1315-26。

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来源期刊

British journal of anaesthesia 医学-麻醉学

CiteScore

13.50

自引率

7.10%

发文量

488

审稿时长

27 days

期刊介绍： The British Journal of Anaesthesia (BJA) is a prestigious publication that covers a wide range of topics in anaesthesia, critical care medicine, pain medicine, and perioperative medicine. It aims to disseminate high-impact original research, spanning fundamental, translational, and clinical sciences, as well as clinical practice, technology, education, and training. Additionally, the journal features review articles, notable case reports, correspondence, and special articles that appeal to a broader audience. The BJA is proudly associated with The Royal College of Anaesthetists, The College of Anaesthesiologists of Ireland, and The Hong Kong College of Anaesthesiologists. This partnership provides members of these esteemed institutions with access to not only the BJA but also its sister publication, BJA Education. It is essential to note that both journals maintain their editorial independence. Overall, the BJA offers a diverse and comprehensive platform for anaesthetists, critical care physicians, pain specialists, and perioperative medicine practitioners to contribute and stay updated with the latest advancements in their respective fields.