We conducted a systematic review of the computing and HCI literature to provide a comprehensive
overview of the current state-of-the-art of machine learning work for mental health.
Searching the ACM Guide to the Computing Literature,
we followed PRISMA review guidelines to identify 54 papers that reported an application of machine
learning in the context of mental health. Our review process was guided by the 6
questions shown here, with the aim to extract current trends and gaps
in research and development in this important and emerging space.
Amongst our findings, we saw an increase in the number of publications in recent years
with the vast majority of papers describing the development of a machine learning model
as their main contribution. Very few papers report on empirical studies, for example of
an end-to-end machine learning intervention. In terms of types of application area, almost
all papers focus on supporting understanding, detection and diagnosis of mental health status.
Here, most common examples include the analysis of acoustic features of speech,
or of Twitter tweets, to help detect or diagnose symptoms of depression or risks of suicide.
In fact, as we can see here, senor data, especially from mobile phone interactions. And
text data, primarily from social media, were the most commonly used data sources for ML analysis.
Aside of efforts to understand, detect or diagnose mental health status,
few works sought to improve mental health treatment or assess patient-client relationships.
But why are we seeing such a dominant focus on mental health problem detection and diagnosis?
Reflecting on this question,
we asked ourselves how problem scenarios for ML are commonly identified?
We can assume that the need for access to large
scale data moderates the types of ML applications that are being developed.
In fact, in most studies uses of data that actually represents patients or people with
a diagnosed mental health condition is often low in numbers with exceptions where the analysis
includes health records or research collaborations involving large health organizations.
Plus, for health detection and diagnosis, outcome measures are more commonly and clearly defined
for example, through clinically validated scales and screening questionnaires.
Thus, we suspect that general difficulties to collect and access high quality mental health data
can limit study design, and force researchers to use readily available data that may be
suboptimal for exploring a particular research question outside of the original data context.
We believe it is important however to not be limited by data access restrictions and instead
to ask: what are the most beneficial (rather than perhaps the most obvious) applications of ML?
To identify other, perhaps even more beneficial uses of ML for mental health requires the
identification of key health care needs through a deeper engagement by researchers with the
real problems that are faced by potential users and health care services on a day-to-day basis.
Also needed are better approaches to negotiating data access by ensuring
that people generally agree to, and can trust researchers and data applications
with the collection and processing of their sensitive information.
Taking these steps towards more multi-disciplinary work and improved data access,
can help broaden the scope of opportunities for ML and its possible impact beyond mental
health detection or categorial diagnosis. This may include for example uses of ML
to more effectively tailor mental health interventions; for treatment optimization;
preventative approaches; and many more. Our analysis further showed that the
majority of papers focused on the technical or algorithmic development of initial ML models,
and most commonly assessed their effectiveness through model performance metrics.
While many of the works “speculate” about the potential impact on clinical
and everyday mental health practices, we caution to not prematurely overclaim
anticipated clinical benefits or generalize too soon from initial proof-of-concepts,
especially where data sets did not include much, or any, clinically validated data.
For future work, there is also a need to study and assess the practical usefulness,
acceptance and effectiveness of developed models for real-world health care practice.
Likely, this requires an extended focus on the challenges of designing new machine learning
enabled systems that are sufficiently interpretable and clinically useful
to its target users or recipients. And for research and development efforts to also
recognize and carefully respond to many of the broader practical and ethical implications that
the use of machine learning systems could have for people, health care, and society