医疗服务提供的大数据和机器学习算法。

PubMed ID
发表日期 2019年05月

原始出处 柳叶刀。肿瘤科
The Lancet. Oncology
作者 Ngiam  Kee Yuan  Khor  Ing Wei 

文献标题 医疗服务提供的大数据和机器学习算法。
Big data and machine learning algorithms for health-care delivery.

文献摘要

利用机器学习对大数据进行分析,为大量复杂医疗数据的同化和评价提供了相当大的优势。然而,要在卫生保健中有效地使用机器学习工具,必须解决一些限制和关键问题,例如其临床实施和卫生保健服务中的伦理问题。与传统的生物统计方法相比,机器学习具有灵活性和可扩展性等优点,可用于风险分层、诊断与分类、生存预测等任务。机器学习算法的另一个优点是能够分析不同的数据类型(例如,人口统计学数据、实验室发现、成像数据和医生的免费文本注释),并将其纳入疾病风险、诊断、预后和适当治疗的预测中。尽管有这些优点,机器学习在医疗服务中的应用也提出了独特的挑战,需要数据预处理、模型训练和针对实际临床问题的系统改进。伦理方面的考虑也很重要,包括医疗法律问题、医生对机器学习工具的理解以及数据隐私和安全。在这篇综述中,我们讨论了大数据和机器学习在医疗保健中的一些好处和挑战。


Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.


获取全文 10.1016/S1470-2045(19)30149-4