机器学习在疼痛学领域的应用现状和展望

Applications and Advances of Machine Learning in Pain Medicine

摘要:

机器学习是一种通过对大量数据进行分析和模式识别来建立模型，实现数据分类、整合、评估和预测等功能的强大工具。机器学习在疼痛学领域的应用展现出巨大的潜力，其与神经影像学等技术的结合为疼痛的客观评估和个体化治疗开拓了新的思路，并为理解疼痛的神经机制提供了更深入的见解。然而，目前已有的机器学习在疼痛学领域的研究方向繁多，彼此间缺少关联性，缺乏对该领域研究的系统性总结，导致相关研究者和临床医师对此仍缺乏全面认识。因此，本文旨在系统性回顾机器学习在疼痛学领域的应用，并讨论当下的挑战和未来发展前景，为相关研究者和临床医师提供系统性认识，也为未来机器学习在疼痛学领域的进一步探索和研究奠定理论基础。

Abstract:

Machine learning has emerged as a potent technique for constructing models through the analysis of extensive datasets, facilitating tasks such as data classification, integration, assessment, and forecasting. Its application within pain medicine has demonstrated significant promise, particularly in conjunction with neuroimaging techniques, which have paved the way for novel methodologies in the objective measurement of pain and the customization of treatment strategies, while also enhancing our understanding of the neurobiological underpinnings of pain. Despite the breadth of research in this domain, there is a notable absence of interconnectedness among studies and a deficiency in comprehensive syntheses of the literature, resulting in a fragmented understanding among the scientific and clinical communities. This review synthesizes the current state of machine learning applications in pain medicine, addresses the prevailing challenges, and examines future directions, aiming to provide a structured overview for researchers and clinicians, and to lay the groundwork for continued investigation and advancement of machine learning techniques in the field.