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A Cross Attention Approach to Diagnostic Explainability Using Clinical Practice Guidelines for Depression
  • +3
  • Sumit Dalal,
  • Deepa Tilwani,
  • Manas Gaur,
  • Sarika Jain,
  • Valerie Shalin,
  • Amit Sheth
Sumit Dalal

Corresponding Author:[email protected]

Author Profile
Deepa Tilwani
Manas Gaur
Sarika Jain
Valerie Shalin
Amit Sheth


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.
30 Jan 2024Submitted to TechRxiv
06 Feb 2024Published in TechRxiv