Participants’ demographic data.

Depression is a serious mental health disorder that poses a major public health concern in Thailand and have a profound impact on individuals’ physical and mental health. In addition, the lack of number to mental health services and limited number of psychiatrists in Thailand make depression particularly challenging to diagnose and treat, leaving many individuals with the condition untreated. Recent studies have explored the use of natural language processing to enable access to the classification of depression, particularly with a trend toward transfer learning from pre-trained language model. In this study, we attempted to evaluate the effectiveness of using XLM-RoBERTa, a pre-trained multi-lingual language model supporting the Thai language, for the classification of depression from a limited set of text transcripts from speech responses. Twelve Thai depression assessment questions were developed to collect text transcripts of speech responses to be used with XLM-RoBERTa in transfer learning. The results of transfer learning with text transcription from speech responses of 80 participants (40 with depression and 40 normal control) showed that when only one question (Q1) of “How are you these days?” was used, the recall, precision, specificity, and accuracy were 82.5%, 84.65, 85.00, and 83.75%, respectively. When utilizing the first three questions from Thai depression assessment tasks (Q1 − Q3), the values increased to 87.50%, 92.11%, 92.50%, and 90.00%, respectively. The local interpretable model explanations were analyzed to determine which words contributed the most to the model’s word cloud visualization. Our findings were consistent with previously published literature and provide similar explanation for clinical settings. It was discovered that the classification model for individuals with depression relied heavily on negative terms such as ‘not,’ ‘sad,’, ‘mood’, ‘suicide’, ‘bad’, and ‘bore’ whereas normal control participants used neutral to positive terms such as ‘recently,’ ‘fine,’, ‘normally’, ‘work’, and ‘working’. The findings of the study suggest that screening for depression can be facilitated by eliciting just three questions from patients with depression, making the process more accessible and less time-consuming while reducing the already huge burden on healthcare workers.抑郁症是一种严重的精神健康障碍，在泰国构成重大公共卫生关切，并对个体的身心健康产生深远影响。此外，泰国精神卫生服务供给不足、精神科医师数量有限，使得抑郁症的诊断与治疗极具挑战，导致众多患者未能得到及时干预。近期已有研究探索利用自然语言处理（Natural Language Processing, NLP）技术实现抑郁症分类，其中从预训练语言模型开展迁移学习的研究趋势愈发显著。本研究旨在评估XLM-RoBERTa（一种支持泰语的预训练多语言语言模型）在基于有限语音回复文本转录本开展抑郁症分类任务中的有效性。研究团队设计了12道泰国抑郁症评估问卷，用于采集语音回复的文本转录本以配合XLM-RoBERTa开展迁移学习。针对80名参与者（40名抑郁症患者与40名正常对照组）的语音回复文本转录本开展迁移学习的结果显示：仅使用第1题“近来过得如何？”时，模型的召回率、精确率、特异度与准确率分别为82.5%、84.65%、85.00%与83.75%。当使用泰国抑郁症评估任务的前3道题目（Q1~Q3）时，上述指标分别提升至87.50%、92.11%、92.50%与90.00%。本研究通过局部可解释模型无关解释（Local Interpretable Model-agnostic Explanations, LIME）分析了对模型词云可视化贡献最大的词汇。研究结果与已发表文献结论一致，可为临床场景提供相似的解释框架。研究发现，抑郁症分类模型高度依赖“not”“sad”“mood”“suicide”“bad”与“bore”等负面词汇，而正常对照组参与者则使用“recently”“fine”“normally”“work”与“working”等中性至积极词汇。本研究结果表明，仅通过向患者询问3道题目即可便捷开展抑郁症筛查，该方式可提升筛查的可及性、缩短流程耗时，同时减轻本已沉重的医护人员工作负担。