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The lack of explainability using relevant clinical knowledge hinders theadoption of Artificial Intelligence-powered analysis of unstructured clinicaldialogue. A wealth of relevant, untapped Mental Health (MH) data is availablein online communities, providing the opportunity to address the explainabilityproblem with substantial potential impact as a screening tool for both onlineand offline applications. We develop a method to enhance attention in populartransformer models and generate clinician-understandable explanations forclassification by incorporating external clinical knowledge. Inspired by howclinicians rely on their expertise when interacting with patients, we leveragerelevant clinical knowledge to model patient inputs, providing meaningfulexplanations for classification. This will save manual review time and engendertrust. We develop such a system in the context of MH using clinical practiceguidelines (CPG) for diagnosing depression, a mental health disorder of globalconcern. We propose an application-specific language model called ProcesSknowledge-infused cross ATtention (PSAT), which incorporates CPGs whencomputing attention. Through rigorous evaluation on three expert-curateddatasets related to depression, we demonstrate application-relevantexplainability of PSAT. PSAT also surpasses the performance of nine baselinemodels and can provide explanations where other baselines fall short. Wetransform a CPG resource focused on depression, such as the Patient HealthQuestionnaire (e.g. PHQ-9) and related questions, into a machine-readableontology using SNOMED-CT. With this resource, PSAT enhances the ability ofmodels like GPT-3.5 to generate application-relevant explanations.

A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression