医疗保健
可扩展性
大数据
计算机科学
机器学习
灵活性(工程)
数据科学
人工智能
数据挖掘
数据库
数学
经济增长
统计
经济
作者
Kee Yuan Ngiam,Ing Wei Khor
出处
期刊:Lancet Oncology
[Elsevier]
日期:2019-05-01
卷期号:20 (5): e262-e273
被引量:916
标识
DOI:10.1016/s1470-2045(19)30149-4
摘要
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.
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