Murali Nikitha, Kucukkaya Ahmet, Petukhova Alexandra, Onofrey John, Chapiro Julius
Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut.
Department of Urology, Yale University School of Medicine, New Haven, Connecticut.
Dig Dis Interv. 2020 Mar;4(1):73-81. doi: 10.1055/s-0040-1705097.
The widespread adoption of electronic health records has resulted in an abundance of imaging and clinical information. New data-processing technologies have the potential to revolutionize the practice of medicine by deriving clinically meaningful insights from large-volume data. Among those techniques is supervised machine learning, the study of computer algorithms that use self-improving models that learn from labeled data to solve problems. One clinical area of application for supervised machine learning is within oncology, where machine learning has been used for cancer diagnosis, staging, and prognostication. This review describes a framework to aid clinicians in understanding and critically evaluating studies applying supervised machine learning methods. Additionally, we describe current studies applying supervised machine learning techniques to the diagnosis, prognostication, and treatment of cancer, with a focus on gastroenterological cancers and other related pathologies.
电子健康记录的广泛采用产生了大量的影像和临床信息。新的数据处理技术有潜力通过从大量数据中获取具有临床意义的见解来彻底改变医学实践。这些技术中包括监督式机器学习，即研究使用从标记数据中学习以解决问题的自我改进模型的计算机算法。监督式机器学习的一个临床应用领域是肿瘤学，机器学习已在其中用于癌症诊断、分期和预后评估。本综述描述了一个框架，以帮助临床医生理解和批判性地评估应用监督式机器学习方法的研究。此外，我们描述了当前将监督式机器学习技术应用于癌症诊断、预后评估和治疗的研究，重点关注胃肠癌和其他相关病理情况。
