Orrù Graziella, Gemignani Angelo, Ciacchini Rebecca, Bazzichi Laura, Conversano Ciro
Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy.
Rheumatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
Front Med (Lausanne). 2020 Jan 13;6:319. doi: 10.3389/fmed.2019.00319. eCollection 2019.
Here, we report an investigation on the accuracy of the Toronto Alexithymia Scale, a measure to assess alexithymia, a multidimensional construct often associate to fibromyalgia. Two groups of participants, patients with fibromyalgia ( = 38), healthy controls ( = 38) were administered the Toronto Alexithymia Scale and background tests. Machine learning models achieved an overall accuracy higher than 80% in detecting both patients with fibromyalgia and healthy controls. The parameter which alone has demonstrated maximum efficiency in classifying the single subject within the two groups has been the item 3 of the alexithymia scale. The analysis of the most informative features, based on all scales administered, revealed that item 3 and 13 of the alexithymia questionnaire and the visual analog scale scores were the most informative attributes in correctly classifying participants (accuracy above 85%). An additional analyses using only the alexithymia scale subset of items and the visual analog scale scores has shown that the predictors which efficiently classified patients with fibromyalgia and controls were the item 3 and 7 (accuracy = 85.53%). Our findings suggest that machine learning models analysis based on the Toronto Alexithymia Scale subset of items scores accurately distinguish patients with fibromyalgia from healthy controls.
在此，我们报告一项关于多伦多述情障碍量表准确性的调查。该量表用于评估述情障碍，这是一种常与纤维肌痛相关的多维结构。两组参与者，即纤维肌痛患者（n = 38）和健康对照者（n = 38），接受了多伦多述情障碍量表及背景测试。机器学习模型在检测纤维肌痛患者和健康对照者方面的总体准确率高于80%。在对两组中的个体进行分类时，单独显示出最高效率的参数是述情障碍量表的第3项。基于所有所施用量表对最具信息量的特征进行分析后发现，述情障碍问卷的第3项和第13项以及视觉模拟量表得分是正确分类参与者的最具信息量的属性（准确率高于85%）。仅使用述情障碍量表项目子集和视觉模拟量表得分进行的另一项分析表明，能够有效区分纤维肌痛患者和对照者的预测指标是第3项和第7项（准确率 = 85.53%）。我们的研究结果表明，基于多伦多述情障碍量表项目子集得分的机器学习模型分析能够准确区分纤维肌痛患者和健康对照者。
