Users of social platforms often perceive these sites as supportive spaces to post about their mental health issues. Those conversations contain important traces about individuals' health risks. Recently, researchers have exploited this online information to construct mental health detection models, which aim to identify users at risk on platforms like Twitter, Reddit or Facebook. Most of these models are centred on achieving good classification results, ignoring the explainability and interpretability of the decisions. Recent research has pointed out the importance of using clinical markers, such as the use of symptoms, to improve trust in the computational models by health professionals. In this paper, we propose using transformer-based architectures to detect and explain the appearance of depressive symptom markers in the users' writings. We present two approaches: i) train a model to classify, and another one to explain the classifier's decision separately and ii) unify the two tasks simultaneously using a single model. Additionally, for this latter manner, we also investigated the performance of recent conversational LLMs when using in-context learning. Our natural language explanations enable clinicians to interpret the models' decisions based on validated symptoms, enhancing trust in the automated process. We evaluate our approach using recent symptom-based datasets, employing both offline and expert-in-the-loop metrics to assess the quality of the explanations generated by our models. The experimental results show that it is possible to achieve good classification results while generating interpretable symptom-based explanations.

翻译：社交平台用户常将这些网站视为分享心理健康问题的支持性空间，这些对话中蕴含着个体健康风险的重要线索。近年来，研究者利用这些在线信息构建心理健康检测模型，旨在识别如Twitter、Reddit或Facebook等平台上的高危用户。现有模型大多聚焦于获得良好的分类结果，却忽视了决策的可解释性与可理解性。近期研究指出，使用临床指标（如症状标识）对于提升医疗专业人员对计算模型的信任至关重要。本文提出基于Transformer架构的方法，用于检测并解释用户文本中抑郁症状标识的出现。我们提出两种方案：(i) 分别训练分类模型与解释分类决策的模型；(ii) 使用单一模型同时完成两项任务。此外，针对后者，我们还研究了近期对话式大语言模型在上下文学习中的表现。通过生成基于已验证症状的自然语言解释，临床医生能够解读模型决策依据，增强对自动化流程的信任。我们采用最新症状数据集进行评估，结合离线指标与专家参与指标衡量模型生成解释的质量。实验结果表明，在生成可解释的症状级解释的同时，仍可实现良好的分类效果。