使用机器学习框架评估肠易激综合征患者自我报告的执行功能

Assessment of Self-Reported Executive Function in Patients with Irritable Bowel Syndrome Using a Machine-Learning Framework.

作者信息

Lundervold Astri J, Hillestad Eline M R, Lied Gülen Arslan, Billing Julie, Johnsen Tina E, Steinsvik Elisabeth K, Hausken Trygve, Berentsen Birgitte, Lundervold Arvid

机构信息

Department of Biological and Medical Psychology, University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway.

Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway.

出版信息

J Clin Med. 2023 May 31;12(11):3771. doi: 10.3390/jcm12113771.

DOI:10.3390/jcm12113771

PMID:37297966

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10253614/

Abstract

: Irritable bowel syndrome (IBS) is characterized as a disorder of the gut-brain interaction (DGBI). Here, we explored the presence of problems related to executive function (EF) in patients with IBS and tested the relative importance of cognitive features involved in EF. : A total of 44 patients with IBS and 22 healthy controls (HCs) completed the Behavior Rating Inventory of Executive Function (BRIEF-A), used to identify nine EF features. The PyCaret 3.0 machine-learning library in Python was used to explore the data, generate a robust model to classify patients with IBS versus HCs and identify the relative importance of the EF features in this model. The robustness of the model was evaluated by training the model on a subset of data and testing it on the unseen, hold-out dataset. : The explorative analysis showed that patients with IBS reported significantly more severe EF problems than the HC group on measures of working memory function, initiation, cognitive flexibility and emotional control. Impairment at a level in need of clinical attention was found in up to 40% on some of these scales. When the nine EF features were used as input to a collection of different binary classifiers, the Extreme Gradient Boosting algorithm (XGBoost) showed superior performance. The working memory subscale was consistently selected with the strongest importance in this model, followed by planning and emotional control. The goodness of the machine-learning model was confirmed in an unseen dataset by correctly classifying 85% of the IBS patients. : The results showed the presence of EF-related problems in patients with IBS, with a substantial impact of problems related to working memory function. These results suggest that EF should be part of an assessment procedure when a patient presents other symptoms of IBS and that working memory function should be considered a target when treating patients with the disorder. Further studies should include measures of EF as part of the symptom cluster characterizing patients with IBS and other DGBIs.

摘要

肠易激综合征（IBS）的特征是肠-脑交互障碍（DGBI）。在此，我们探究了IBS患者中与执行功能（EF）相关问题的存在情况，并测试了参与EF的认知特征的相对重要性。

共有44例IBS患者和22名健康对照（HCs）完成了执行功能行为评定量表（BRIEF-A），该量表用于识别9种EF特征。使用Python中的PyCaret 3.0机器学习库来探索数据，生成一个强大的模型以区分IBS患者与HCs，并确定该模型中EF特征的相对重要性。通过在一部分数据上训练模型并在未见过的保留数据集中对其进行测试来评估模型的稳健性。

探索性分析表明，在工作记忆功能、启动、认知灵活性和情绪控制方面，IBS患者报告的EF问题比HC组严重得多。在其中一些量表上，高达40%的患者存在需要临床关注程度的损害。当将9种EF特征用作不同二元分类器集合的输入时，极端梯度提升算法（XGBoost）表现出卓越的性能。在该模型中，工作记忆子量表始终被选为最重要的因素，其次是计划和情绪控制。通过正确分类85%的IBS患者，在一个未见过的数据集中证实了机器学习模型的有效性。

结果表明IBS患者存在与EF相关的问题，工作记忆功能相关问题有重大影响。这些结果表明，当患者出现IBS的其他症状时，EF应成为评估程序的一部分，并且在治疗该疾病患者时应将工作记忆功能视为一个靶点。进一步的研究应将EF测量作为表征IBS和其他DGBIs患者症状群的一部分。