One of the primary challenges faced by researchers and clinicians seeking to study mental health is that direct observation of indicators of mental health issues can be challenging, as a diagnosis often relies on either self-reporting of specific feelings or actions, or direct observation of a subject (which can be difficult due to time and cost considerations). That is why there has been a specific focus over the past two decades on deploying technology to help human clinicians identify and assess mental health issues.
Between 2000 and 2019, 54 academic papers focused on the development of machine learning systems to help diagnose and address mental health issues were published, according to a 2020 article published in ACM Transactions on Computer-Human Interaction. Of the 54 papers, 40 focused on the development of a machine learning (ML) model based on specific data as their main research contribution, while seven were proposals of specific concepts, data methods, models, or systems, and three applied existing ML algorithms to better understand and assess mental health, or improve the communication of mental health providers. A few of the papers described the conduct of empirical studies of an end-to-end ML system or assessed the quality of ML predictions, while one paper specifically discusses design implications for user-centric, deployable ML systems.
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