机器学习概论：利用大数据集分析实现疼痛管理个体化。
Primer on machine learning: utilization of large data set analyses to individualize pain management.
机构信息
J. Crayton Pruitt Family Department of Biomedical Engineering (BME), University of Florida (UF).
Division of Pain Medicine, Department of Anesthesiology, Vanderbilt University, Nashville, TN.
出版信息
Curr Opin Anaesthesiol. 2019 Oct;32(5):653-660. doi: 10.1097/ACO.0000000000000779.
Abstract
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.
摘要
目的综述
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。本综述提供了对关键机器学习原则的总结，以及对结构化和非结构化数据集的应用。
最近的发现
除了在电子健康记录数据的分析中使用增加外，机器学习和深度学习算法现在是疼痛研究中神经影像学和面部表情识别数据分析的关键工具。
总结
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，但在研究和临床环境中成功和合乎道德地使用它还需要额外的技能和观点。
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