Language models have the potential to assess mental health using social media data. By analyzing online posts and conversations, these models can detect patterns indicating mental health conditions like depression, anxiety, or suicidal thoughts. They examine keywords, language markers, and sentiment to gain insights into an individual's mental well-being. This information is crucial for early detection, intervention, and support, improving mental health care and prevention strategies. However, using language models for mental health assessments from social media has two limitations: (1) They do not compare posts against clinicians' diagnostic processes, and (2) It's challenging to explain language model outputs using concepts that the clinician can understand, i.e., clinician-friendly explanations. In this study, we introduce Process Knowledge-infused Learning (PK-iL), a new learning paradigm that layers clinical process knowledge structures on language model outputs, enabling clinician-friendly explanations of the underlying language model predictions. We rigorously test our methods on existing benchmark datasets, augmented with such clinical process knowledge, and release a new dataset for assessing suicidality. PK-iL performs competitively, achieving a 70% agreement with users, while other XAI methods only achieve 47% agreement (average inter-rater agreement of 0.72). Our evaluations demonstrate that PK-iL effectively explains model predictions to clinicians.

翻译：语言模型有潜力利用社交媒体数据评估心理健康状况。通过分析在线帖子和对话，这些模型能够检测出表明抑郁、焦虑或自杀意念等心理健康问题的模式。它们通过考察关键词、语言标记和情感倾向来洞察个体的心理状态。这些信息对早期检测、干预和支持至关重要，有助于改善心理医疗保健和预防策略。然而，利用语言模型从社交媒体进行心理健康评估存在两个局限：（1）它们未将帖子与临床医生的诊断过程进行对比；（2）难以使用临床医生能理解的概念（即临床友好型解释）来解释语言模型的输出。在本研究中，我们提出过程知识注入式学习（PK-iL），这是一种新的学习范式，将临床过程知识结构分层嵌入语言模型输出，从而实现对底层语言模型预测的临床友好型解释。我们在现有基准数据集上严格测试了该方法，并用此类临床过程知识进行增强，同时发布了一个用于评估自杀倾向的新数据集。PK-iL表现出竞争力，与用户的共识度达到70%，而其他XAI方法仅达到47%的共识度（平均评估者间一致性为0.72）。我们的评估表明，PK-iL能有效地向临床医生解释模型预测结果。