A Cross Attention Approach to Diagnostic Explainability Using Clinical Practice Guidelines for Depression
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Dalal, Sumit, Deepa Tilwani, Manas Gaur, Sarika Jain, Valerie L. Shalin, and Amit P. Sheth. “A Cross Attention Approach to Diagnostic Explainability Using Clinical Practice Guidelines for Depression.” IEEE Journal of Biomedical and Health Informatics 29, no. 2 (2024): 1333–42. https://doi.org/10.1109/JBHI.2024.3483577.
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Abstract
The lack of explainability in 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. Inspired by how clinicians rely on their expertise when interacting with patients, we leverage relevant clinical knowledge to classify and explain depression-related data, reducing manual review time and engendering trust. We developed a method to enhance attention in contemporary transformer models and generate explanations for classifications that are understandable by mental health practitioners (MHPs) by incorporating external clinical knowledge. We propose a domain-general architecture called ProcesS knowledge-infused cross ATtention (PSAT) that incorporates clinical practice guidelines (CPG) when computing attention. 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. Evaluation of four expert-curated datasets related to depression demonstrates PSAT’s application-relevant explanations. PSAT surpasses the performance of twelve baseline models and can provide explanations where other baselines fall short.