Suppr超能文献

临床实践中的机器学习模型预测腹部大手术后的术后并发症。

Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery.

机构信息

Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.

Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands.

出版信息

Surg Today. 2023 Oct;53(10):1209-1215. doi: 10.1007/s00595-023-02662-4. Epub 2023 Feb 25.

Abstract

Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.

摘要

手术后的并发症对短期和长期结果有重大影响,尽管几十年来技术不断进步,但仍未能消除其风险。最近,机器学习算法的发展提高了并发症预测的准确性,有可能彻底改变外科患者的管理方式。本文从机器学习模型的开发到在日常临床实践中的实际应用,反思了机器学习实施所面临的多个问题,并就如何使用机器学习模型预测腹部大手术后的术后并发症提供了建议。

相似文献

本文引用的文献

7
Success Factors of Artificial Intelligence Implementation in Healthcare.医疗保健领域人工智能实施的成功因素。
Front Digit Health. 2021 Jun 16;3:594971. doi: 10.3389/fdgth.2021.594971. eCollection 2021.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验