Machine Learning in Mental Health

Abstract

High prevalence of mental illness and the need for effective mental health care, combined with recent advances in AI, has led to an increase in explorations of how the field of machine learning (ML) can assist in the detection, diagnosis and treatment of mental health problems. ML techniques can potentially offer new routes for learning patterns of human behavior; identifying mental health symptoms and risk factors; developing predictions about disease progression; and personalizing and optimizing therapies. Despite the potential opportunities for using ML within mental health, this is an emerging research area, and the development of effective ML-enabled applications that are implementable in practice is bound up with an array of complex, interwoven challenges. Aiming to guide future research and identify new directions for advancing development in this important domain, this article presents an introduction to, and a systematic review of, current ML work regarding psycho-socially based mental health conditions from the computing and HCI literature. A quantitative synthesis and qualitative narrative review of 54 papers that were included in the analysis surfaced common trends, gaps, and challenges in this space. Discussing our findings, we (i) reflect on the current state-of-the-art of ML work for mental health, (ii) provide concrete suggestions for a stronger integration of human-centered and multidisciplinary approaches in research and development, and (iii) invite more consideration of the potentially far-reaching personal, social, and ethical implications that ML models and interventions can have, if they are to find widespread, successful adoption in real-world mental health contexts.

Is the Automation of Digital Mental Health Ethical? Applying an Ethical Framework to Chatbots for Cognitive Behaviour Therapy

“…Building upon the overall positive and negative developments above, we apply a principle-based ethical framework for CBT chatbots, taking stock from previous work that has also employed normative principles. We found pertinence in the principles of beneficence, non-maleficence, autonomy, justice, and explicability-previously used in a typology for AI-ethics in general (29); and in the structure of findings from a systematic review of machine learning for mental health (30). Despite the relevance of these previous works, they are not sufficient to attend to the particularities of CBT chatbots, which demands discussions of the appropriateness of artificially produced therapeutic alliances, for instance.…”

“…To better attend to the principle of non-maleficence, a thorough analysis of potential risks to mental and physical integrity, dignity, and safety needs to be conducted (30). Ethical professionals' engagement in defining the appropriate boundaries of personalised care using digital tools should be a minimum requirement (62); and vulnerable persons should be consulted during design, development, and deployment (63).…”

Unaccompanied Migrant Youth and Mental Health Technologies: A Social-Ecological Approach to Understanding and Designing

“…The passively sensed data could be complemented and improved with actively collected data via self-reporting solutions such as ecological momentary assessments [27]. However, as Thieme et al [79] have indicated in their review of ML in mental health, much work is needed in understanding what data individuals are willing to share passively and actively to design appropriately responsive ML driven mental health technologies. Our fndings show that UMY have high privacy concerns.…”

Predicting compassion fatigue among psychological hotline counselors using machine learning techniques

“…With respect to the application in psychology, classification algorithms have been proven to be highly effective to predict participants' mental status. A recent systematic review summarized the application of machine learning in the area of mental health and suggested that the performance of classification algorithms was satisfied in the prediction of depression, suicide, job-related stress, bipolar disorder, mood, posttraumatic stress disorder, anxiety, substance abuse, and schizophrenia (Thieme et al, 2020). According to the "No Free Lunch Theorem", no single algorithm is consistently superior to the others (Wolpert & Macready, 1997).…”