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一种用于心理健康对话代理的进化自展开发方法。

An Evolutionary Bootstrapping Development Approach for a Mental Health Conversational Agent.

作者信息

Househ Mowafa, Schneider Jens, Ahmad Kashif, Alam Tanvir, Al-Thani Dena, Siddig Mohamed Ali, Fernandez-Luque Luis, Qaraqe Marwa, Alfuquha Ala, Saxena Shekhar

机构信息

Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Doha, Qatar.

Community Mental Health Services, Hamad Medical Corporation, Doha, Qatar.

出版信息

Stud Health Technol Inform. 2019 Jul 4;262:228-231. doi: 10.3233/SHTI190060.

Abstract

Conversational agents are being used to help in the screening, assessment, diagnosis, and treatment of common mental health disorders. In this paper, we propose a bootstrapping approach for the development of a digital mental health conversational agent (i.e., chatbot). We start from a basic rule-based expert system and iteratively move towards a more sophisticated platform composed of specialized chatbots each aiming to assess and pre-diagnose a specific mental health disorder using machine learning and natural language processing techniques. During each iteration, user feedback from psychiatrists and patients are incorporated into the iterative design process. A survival of the fittest approach is also used to assess the continuation or removal of a specialized mental health chatbot in each generational design. We anticipate that our unique and novel approach can be used for the development of conversational mental health agents with the ultimate goal of designing a smart chatbot that delivers evidence-based care and contributes to scaling up services while decreasing the pressure on mental health care providers.

摘要

对话代理正被用于帮助筛查、评估、诊断和治疗常见的心理健康障碍。在本文中,我们提出了一种用于开发数字心理健康对话代理(即聊天机器人)的自训练方法。我们从一个基于基本规则的专家系统开始,逐步迭代发展为一个更复杂的平台,该平台由专门的聊天机器人组成,每个聊天机器人都旨在使用机器学习和自然语言处理技术来评估和预诊断特定的心理健康障碍。在每次迭代过程中,将精神科医生和患者的用户反馈纳入迭代设计过程。在每一代设计中,还采用适者生存的方法来评估特定心理健康聊天机器人是否继续存在或被淘汰。我们预计,我们独特新颖的方法可用于开发对话式心理健康代理,最终目标是设计一个提供循证护理的智能聊天机器人,在减轻心理健康护理提供者压力的同时扩大服务规模。

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