Primer on machine learning: utilization of large data set analyses to individualize pain management.
Primer on machine learning: utilization of large data set analyses to individualize pain management.
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DOI：
10.1097/aco.0000000000000779
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Parisa Rashidi;David A. Edwards;P. Tighe
中科院分区：
文献类型：
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作者：
Parisa Rashidi;David A. Edwards;P. Tighe
疼痛研究人员和临床医生在研究方法和临床实践中越来越多地遇到机器学习算法。这篇综述总结了机器学习的关键原则，以及在结构化和非结构化数据集上的应用。除了越来越多地用于分析电子健康记录数据外，机器和深度学习算法现在是分析用于疼痛研究的神经成像和面部表情识别数据的关键工具。在未来几年，机器学习很可能成为循证医学的一个关键组成部分，但需要额外的技能和观点，才能在研究和临床环境中成功和道德地使用。
PURPOSE OF REVIEW Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as well as applications to both structured and unstructured datasets. RECENT FINDINGS Aside from increasing use in the analysis of electronic health record data, machine and deep learning algorithms are now key tools in the analyses of neuroimaging and facial expression recognition data used in pain research. SUMMARY In the coming years, machine learning is likely to become a key component of evidence-based medicine, yet will require additional skills and perspectives for its successful and ethical use in research and clinical settings.