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机器学习在肿瘤学中的应用:临床评价。

Machine Learning in oncology: A clinical appraisal.

机构信息

Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, 80131, Naples, Italy.

Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, 80131, Naples, Italy.

出版信息

Cancer Lett. 2020 Jul 1;481:55-62. doi: 10.1016/j.canlet.2020.03.032. Epub 2020 Apr 3.


DOI:10.1016/j.canlet.2020.03.032
PMID:32251707
Abstract

Machine learning (ML) is a branch of artificial intelligence centered on algorithms which do not need explicit prior programming to function but automatically learn from available data, creating decision models to complete tasks. ML-based tools have numerous promising applications in several fields of medicine. Its use has grown following the increased availability of patient data due to technological advances such as digital health records and high-volume information extraction from medical images. Multiple ML algorithms have been proposed for applications in oncology. For instance, they have been employed for oncological risk assessment, automated segmentation, lesion detection, characterization, grading and staging, prediction of prognosis and therapy response. In the near future, ML could become essential part of every step of oncological screening strategies and patients' management thus leading to precision medicine.

摘要

机器学习(ML)是人工智能的一个分支,其核心算法无需事先明确编程即可运行,而是能够自动从可用数据中学习,创建决策模型以完成任务。基于机器学习的工具在医学的多个领域有许多有前途的应用。随着数字健康记录和从医学图像中提取大量信息等技术的进步,患者数据的可用性增加,其使用也有所增加。已经提出了多种机器学习算法来应用于肿瘤学。例如,它们已被用于肿瘤风险评估、自动分割、病灶检测、特征描述、分级和分期、预后和治疗反应预测。在不久的将来,机器学习可能成为肿瘤学筛查策略和患者管理的每一个步骤的重要组成部分,从而实现精准医疗。

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