The lack of explainability using relevant clinical knowledge hinders the
adoption of Artificial Intelligence-powered analysis of unstructured clinical
dialogue. A wealth of relevant, untapped Mental Health (MH) data is available
in online communities, providing the opportunity to address the explainability
problem with substantial potential impact as a screening tool for both online
and offline applications. We develop a method to enhance attention in popular
transformer models and generate clinician-understandable explanations for
classification by incorporating external clinical knowledge. Inspired by how
clinicians rely on their expertise when interacting with patients, we leverage
relevant clinical knowledge to model patient inputs, providing meaningful
explanations for classification. This will save manual review time and engender
trust. We develop such a system in the context of MH using clinical practice
guidelines (CPG) for diagnosing depression, a mental health disorder of global
concern. We propose an application-specific language model called ProcesS
knowledge-infused cross ATtention (PSAT), which incorporates CPGs when
computing attention. Through rigorous evaluation on three expert-curated
datasets related to depression, we demonstrate application-relevant
explainability of PSAT. PSAT also surpasses the performance of nine baseline
models and can provide explanations where other baselines fall short. We
transform a CPG resource focused on depression, such as the Patient Health
Questionnaire (e.g. PHQ-9) and related questions, into a machine-readable
ontology using SNOMED-CT. With this resource, PSAT enhances the ability of
models like GPT-3.5 to generate application-relevant explanations.

通过加入临床知识，PSAT 模型提出了一种解释性的人工智能方法来分析和分类临床对话，为诊断抑郁症等心理健康问题提供了应用相关的解释。