Data Engineering for Machine Learning in Women's Imaging and Beyond.
Data Engineering for Machine Learning in Women's Imaging and Beyond.
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DOI：
10.2214/ajr.18.20464
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发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
Samir AE
中科院分区：
文献类型：
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作者：
Cui C;Chou SS;Brattain L;Lehman CD;Samir AE
数据工程是有效的机器学习模型开发和研究的基础。机器学习模型的准确性和临床实用性从根本上取决于用于模型开发的数据的质量。本文旨在帮助放射科医生和放射学研究人员了解机器学习研究数据准备的核心要素。我们从工程角度涵盖关键概念，包括数据库、数据完整性和适合机器学习项目的数据特征，从临床角度涵盖关键概念，包括 HIPAA、患者同意、避免偏见以及与扩大健康差异的可能性相关的伦理问题。本文的重点是女性影像；尽管如此，所描述的原则适用于医学成像的所有领域。机器学习研究本质上是跨学科的：有效的协作对于成功至关重要。在医学成像领域，放射科医生拥有数据工程师所必需的知识，可以为机器学习模型开发开发有用的数据集。
Data engineering is the foundation of effective machine learning model development and research. The accuracy and clinical utility of machine learning models fundamentally depend on the quality of the data used for model development. This article aims to provide radiologists and radiology researchers with an understanding of the core elements of data preparation for machine learning research. We cover key concepts from an engineering perspective, including databases, data integrity, and characteristics of data suitable for machine learning projects, and from a clinical perspective, including the HIPAA, patient consent, avoidance of bias, and ethical concerns related to the potential to magnify health disparities. The focus of this article is women’s imaging; nonetheless, the principles described apply to all domains of medical imaging. Machine learning research is inherently interdisciplinary: effective collaboration is critical for success. In medical imaging, radiologists possess knowledge essential for data engineers to develop useful datasets for machine learning model development.