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.

通过使用 Proknow 和具有引导作用的自然语言问答生成方法，我们基于 449 个抑郁症和焦虑症患者的诊断谈话开发出一种具有安全约束和专业知识的诊断对话数据集。我们证明了在这个数据集上使用最先进的大规模语言模型的局限性，通过明确建模安全、知识捕获和可解释性，我们的算法提高了 82％以上的生成问题的安全性、解释性和过程指导。

ProKnow: 面向安全约束和可解释性的过程知识用于心理健康诊断辅助问答生成

ProKnow: Process Knowledge for Safety Constrained and Explainable  Question Generation for Mental Health Diagnostic Assistance

Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to
support the overburdened global healthcare system that gets 60 million primary
care visits, and 6 million Emergency Room (ER) visits annually. These systems
are built by clinical psychologists, psychiatrists, and Artificial Intelligence
(AI) researchers for Cognitive Behavioral Therapy (CBT). At present, the role
of VMHAs is to provide emotional support through information, focusing less on
developing a reflective conversation with the patient. A more comprehensive,
safe and explainable approach is required to build responsible VMHAs to ask
follow-up questions or provide a well-informed response. This survey offers a
systematic critical review of the existing conversational agents in mental
health, followed by new insights into the improvements of VMHAs with contextual
knowledge, datasets, and their emerging role in clinical decision support. We
also provide new directions toward enriching the user experience of VMHAs with
explainability, safety, and wholesome trustworthiness. Finally, we provide
evaluation metrics and practical considerations for VMHAs beyond the current
literature to build trust between VMHAs and patients in active communications.

通过系统性且批判性的回顾现有的心理健康对话系统，在上下文知识、数据集和临床决策支持方面提供全面、安全、可解释和值得信赖的虚拟心理健康助手（VMHAs）。

面向心理健康的可解释和安全对话代理：一项调查

Towards Explainable and Safe Conversational Agents for Mental Health: A  Survey

Virtual Mental Health Assistants (VMHAs) have become a prevalent method for
receiving mental health counseling in the digital healthcare space. An
assistive counseling conversation commences with natural open-ended topics to
familiarize the client with the environment and later converges into more
fine-grained domain-specific topics. Unlike other conversational systems, which
are categorized as open-domain or task-oriented systems, VMHAs possess a hybrid
conversational flow. These counseling bots need to comprehend various aspects
of the conversation, such as dialogue-acts, intents, etc., to engage the client
in an effective conversation. Although the surge in digital health research
highlights applications of many general-purpose response generation systems,
they are barely suitable in the mental health domain -- the prime reason is the
lack of understanding in mental health counseling. Moreover, in general,
dialogue-act guided response generators are either limited to a template-based
paradigm or lack appropriate semantics. To this end, we propose READER -- a
REsponse-Act guided reinforced Dialogue genERation model for the mental health
counseling conversations. READER is built on transformer to jointly predict a
potential dialogue-act d(t+1) for the next utterance (aka response-act) and to
generate an appropriate response u(t+1). Through the
transformer-reinforcement-learning (TRL) with Proximal Policy Optimization
(PPO), we guide the response generator to abide by d(t+1) and ensure the
semantic richness of the responses via BERTScore in our reward computation. We
evaluate READER on HOPE, a benchmark counseling conversation dataset and
observe that it outperforms several baselines across several evaluation metrics
-- METEOR, ROUGE, and BERTScore. We also furnish extensive qualitative and
quantitative analyses on results, including error analysis, human evaluation,
etc.

本研究提出了一种基于 READER 的对话操作引导加强型对话生成模型，用于基于虚拟心理健康助手的辅助心理咨询对话，该模型在对话 - 操作引导下能够生成更具语义丰富度的反应，经过了对多种评估方法的考察，表现优异。