Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because they lack training in safety-constrained and specialized clinical process knowledge. 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. We develop a method for natural language question generation (NLG) that collects diagnostic information from the patient interactively. We demonstrate the limitations of using state-of-the-art large-scale language models (LMs) on this dataset. Our algorithm models the process knowledge through explicitly modeling safety, knowledge capture, and explainability. LMs augmented with ProKnow guided method generated 89% safer questions in the depression and anxiety domain. 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 augmented with our ProKnow, we achieved an average 82% improvement over simple pre-trained LMs on safety, explainability, and process-guided question generation. We qualitatively and quantitatively evaluate the efficacy of the proposed ProKnow-guided methods by introducing three new evaluation metrics for safety, explainability, and process knowledge adherence.

翻译：当前虚拟心理健康助手（VMHAs）提供咨询和建议性护理，但因其缺乏安全约束与专业临床流程知识的训练，无法进行患者诊断辅助。本文定义ProKnow为按顺序排列的信息集合，该集合映射至领域专家所采用的循证指南或概念理解类别。我们同时引入一个由安全约束和ProKnow引导的医疗专业人员使用的诊断对话新数据集。我们开发了一种自然语言问题生成（NLG）方法，可从患者处交互式采集诊断信息。我们证明了在此数据集上使用最先进的大规模语言模型（LMs）的局限性。我们的算法通过对安全性、知识获取和可解释性的显式建模，实现对流程知识的建模。经ProKnow引导方法增强的LMs在抑郁和焦虑领域生成了89%更安全的问题。通过计算生成问题与抑郁及焦虑知识库中概念的相似度，评估了问题的可解释性。总体而言，无论使用何种类型的LMs，经ProKnow增强后，在安全性、可解释性和流程引导的问题生成方面，相较于简单预训练的LMs平均提升82%。我们通过引入三项新的评估指标（安全性、可解释性和流程知识遵循度），定性与定量地评估了所提出的ProKnow引导方法的有效性。