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Machine learning methods for automatic pain assessment using facial expression information

机译:使用面部表情信息进行自动疼痛评估的机器学习方法

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摘要

Introduction:Prediction of pain using machine learning algorithms is an emerging field in both computer science and clinical medicine. Several machine algorithms were developed and validated in recent years. However, the majority of studies in this topic was published on bioinformatics or computer science journals instead of medical journals. This tendency and preference led to a gap of knowledge and acknowledgment between computer scientists who invent the algorithm and medical researchers who may use the algorithms in practice. As a consequence, some of these prediction papers did not discuss the clinical utility aspects and were causally reported without following related professional guidelines (e.g., TRIPOD statement). The aim of this protocol is to systematically summarize the current evidences about performance and utility of different machine learning methods used for automatic pain assessments based on human facial expression. In addition, this study is aimed to demonstrate and fill the knowledge gap to promote interdisciplinary collaboration.
机译:简介:使用机器学习算法预测疼痛是计算机科学和临床医学中的一个新兴领域。近年来,开发并验证了几种机器算法。但是,有关该主题的大多数研究是在生物信息学或计算机科学杂志上发表的,而不是医学杂志上发表的。这种趋势和偏好导致发明该算法的计算机科学家与可能在实践中使用该算法的医学研究人员之间的知识和认知差距。结果,其中一些预测论文没有讨论临床实用性方面,并且因果报导而未遵循相关专业准则(例如TRIPOD声明)。该协议的目的是系统地总结有关用于基于人脸表情的自动疼痛评估的不同机器学习方法的性能和效用的当前证据。此外,本研究旨在证明并填补知识空白,以促进跨学科合作。

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