The University of Melbourne School of Computing and Information Systems, Australia.
Curr Opin Psychol. 2020 Dec;36:112-117. doi: 10.1016/j.copsyc.2020.04.005. Epub 2020 Jun 3.
With the advent of digital approaches to mental health, modern artificial intelligence (AI), and machine learning in particular, is being used in the development of prediction, detection and treatment solutions for mental health care. In terms of treatment, AI is being incorporated into digital interventions, particularly web and smartphone apps, to enhance user experience and optimise personalised mental health care. In terms of prediction and detection, modern streams of abundant data mean that data-driven AI methods can be employed to develop prediction/detection models for mental health conditions. In particular, an individual's 'digital exhaust', the data gathered from their numerous personal digital device and social media interactions, can be mined for behavioural or mental health insights. Language, long considered a window into the human mind, can now be quantitatively harnessed as data with powerful computer-based natural language processing to also provide a method of inferring mental health. Furthermore, natural language processing can also be used to develop conversational agents used for therapeutic intervention.
随着数字方法在精神健康领域的出现,现代人工智能(AI),特别是机器学习,正在被用于开发精神健康护理的预测、检测和治疗解决方案。在治疗方面,人工智能被整合到数字干预措施中,特别是网络和智能手机应用程序中,以增强用户体验和优化个性化精神健康护理。在预测和检测方面,大量现代数据流意味着可以使用基于数据的 AI 方法来开发精神健康状况的预测/检测模型。特别是,可以从个人的众多个人数字设备和社交媒体交互中挖掘“数字痕迹”,以获取行为或精神健康见解。语言长期以来一直被视为了解人类思维的窗口,现在可以通过强大的基于计算机的自然语言处理技术对其进行量化利用,从而提供一种推断精神健康的方法。此外,自然语言处理还可用于开发用于治疗干预的会话代理。