ProKnow: Process knowledge for safety constrained and explainable question generation for mental health diagnostic assistance - Roy, Kaushik; Gaur, Manas; Soltani, Misagh; Rawte, Vipula; Kalyan, Ashwin; Sheth, Amit (Frontiers in Big Data, 2023)
Virtual Mental Health Assistants (VMHAs) are utilized in health care to provide patient services such as counseling and suggestive care. They are not used for patient diagnostic assistance because they cannot adhere to safety constraints and specialized clinical process knowledge (<sans-serif>ProKnow</sans-serif>) used to obtain clinical diagnoses. In this work, we define <sans-serif>ProKnow</sans-serif> 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 <sans-serif>ProKnow</sans-serif> that healthcare professionals use (<sans-serif>ProKnow</sans-serif>-data). We develop a method for natural language question generation (NLG) that collects diagnostic information from the patient interactively (<sans-serif>ProKnow</sans-serif>-algo). We demonstrate the limitations of using state-of-the-art large-scale language models (LMs) on this dataset. <sans-serif>ProKnow</sans-serif>-algo incorporates the process knowledge through explicitly modeling safety, knowledge capture, and explainability. As computational metrics for evaluation do not directly translate to clinical settings, we involve expert clinicians in designing evaluation metrics that test four properties: safety, logical coherence, and knowledge capture for explainability while minimizing the standard cross entropy loss to preserve distribution semantics-based similarity to the ground truth. LMs with <sans-serif>ProKnow</sans-serif>-algo generated 89% safer questions in the depression and anxiety domain (tested property: safety). Further, without <sans-serif>ProKnow</sans-serif>-algo generations question did not adhere to clinical process knowledge in <sans-serif>ProKnow</sans-serif>-data (tested property: knowledge capture). In comparison, <sans-serif>ProKnow</sans-serif>-algo-based generations yield a 96% reduction in our metrics to measure knowledge capture. 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, <sans-serif>ProKnow</sans-serif>-algo achieved an averaged 82% improvement over simple pre-trained LMs on safety, explainability, and process-guided question generation. For reproducibility, we will make <sans-serif>ProKnow</sans-serif>-data and the code repository of <sans-serif>ProKnow</sans-serif>-algo publicly available upon acceptance.