Document Type

Article

Abstract

Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because of a lack of training on safety-constrained and specialized clinical process knowledge (Pro-Know). In this work, we define ProKnow as an ordered set of information that maps to evidence-based guidelines or categories of conceptual understanding to experts in a domain. We also introduce a new dataset of diagnostic conversations guided by safety constraints and ProKnow that healthcare professionals use (ProKnow-data). We develop a method for natural language question generation (NLG) that collects diagnostic information from the patient interactively (ProKnow-algo). We demonstrate the limitations of using state-of-the-art large-scale language models (LMs) on this dataset. ProKnow-algo models the process knowledge through explicitly modeling safety, knowledge capture, and explainability. LMs with ProKnow-algo generated 89% safer questions in the depression and anxiety domain. Further, without ProKnow-algo generations question did not adhere to clinical process knowledge in ProKnow-data. In comparison, ProKnow-algo-based generations yield a 96% reduction in averaged squared rank error. The Explainability of the generated question is assessed by computing similarity with concepts in depression and anxiety knowledge bases. Overall, irrespective of the type of LMs, ProKnow-algo achieved an averaged 82% improvement over simple pre-trained LMs on safety, explainability, and process-guided question generation. We qualitatively and quantitatively evaluate the efficacy of ProKnow-algo by introducing three new evaluation metrics for safety, explainability, and process knowledge-adherence. For reproducibility, we will make ProKnow-data and the code repository of ProKnow-algo publicly available upon acceptance.

APA Citation

Roy, K., Gaur, M., Rawte, V., Kalyan, A., & Sheth, A. (2022). ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance. [Preprint]

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