Analysis of chronic pain descriptions for base-pathology prediction: the case of rheumatoid arthritis versus spondylitis pathology prediction based on pain descriptions

Revista Dor Pub Date : 2022-07-08 DOI:10.24875/dor.m22000014
D. Nunes, J. Ferreira-Gomes, C. Vaz, Daniel de Oliveira, S. Pimenta, F. Neto, David Martins de Matos
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引用次数: 2

Abstract

The language of pain is a sub-language used to describe a subjective, private, and painful experience. In the clinical assessment and management of chronic pain, which is not as straightforward as acute pain, verbal communication is key to convey relevant information to health professionals that would otherwise not be accessible, namely, intrinsic qualities of the painful experience and that of the patient. We raise the hypothesis of applying Natural Language Processing techniques to transcribed verbal descriptions of chronic pain, to capture that information in the form of linguistic features that characterize and quantify the experience of pain of each patient. Furthermore, we demonstrate the application of these features for base-pathology prediction, specifically regarding the diagnosis of rheumatoid arthritis and spondyloarthritis. A dataset of verbal descriptions was collected for this work, considering 85 patients. The descriptions were obtained by having each patient freely answer to an interview of seven questions. The dataset was pre-processed, and features were extracted, which were then fed into binary classification machine learning models. We obtained an accuracy of 79%, in a Leave-One-Out cross-validation fashion. Based on an extensive experimental setup, we conclude that the computational analysis of the language of pain can potentially extract useful information to aid health professionals, in this case, focusing on base-pathology prediction. We also conclude on which semantic features provided more useful information for the task (distribution of pain on the body), and which did not.
慢性疼痛描述对基础病理预测的分析:类风湿关节炎与脊柱炎病理预测基于疼痛描述的案例
痛苦的语言是一种亚语言,用来描述主观的、私人的、痛苦的经历。慢性疼痛不像急性疼痛那样直截了当,在慢性疼痛的临床评估和管理中,语言交流是向卫生专业人员传达相关信息的关键,否则这些信息是无法获得的,即疼痛体验和患者的内在品质。我们提出了应用自然语言处理技术来转录慢性疼痛的口头描述的假设,以语言特征的形式捕获信息,表征和量化每个病人的疼痛体验。此外,我们还展示了这些特征在基础病理学预测中的应用,特别是在类风湿关节炎和脊椎关节炎的诊断方面。这项工作收集了85名患者的语言描述数据集。这些描述是通过让每位患者自由回答七个问题的访谈获得的。对数据集进行预处理,提取特征,然后将其输入到二元分类机器学习模型中。我们获得了79%的准确度,以留一种交叉验证的方式。基于广泛的实验设置,我们得出结论,疼痛语言的计算分析可以潜在地提取有用的信息,以帮助卫生专业人员,在这种情况下,专注于基础病理预测。我们还总结了哪些语义特征为任务提供了更有用的信息(身体上疼痛的分布),哪些没有。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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