Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Mar 15, 2022)
Date Submitted: Jul 27, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Overview of the role of big data in mental health: A scoping review
ABSTRACT
Background:
Big Data offers promise in the field of mental health and plays an important part when it comes to automation, analysis and prevention of mental health disorders
Objective:
The purpose of this scoping review is to explore how big data was exploited in mental health. This review specifically addresses both the volume, velocity, veracity and variety of collected data as well as how data was attained, stored, managed, and kept private and secure.
Methods:
Six databases were searched to find relevant articles. PRISMA Extension for Scoping Reviews (PRISMA-ScR) was used as a guideline methodology to develop a comprehensive scoping review.
Results:
General and Big Data features were extracted from the studies reviewed. Various technologies were noted when it comes to using Big Data in mental health with depression and anxiety being the focus of most of the studies. Some of these included Machine Learning (ML) models in 22 studies of which Random Forest (RF) was the most widely used. Logistic Regression (LR) was used in 4 studies, and Support Vector Machine (SVM) was used in 3 studies.
Conclusions:
In order to utilize Big Data as a way to mitigate mental health disorders and prevent their appearance altogether a great effort is still needed. Integration and analysis of Big Data, doctors and researchers alike can find patterns in otherwise difficult to identify data by making use of AI and Machine Learning techniques. Similarly, machine learning and artificial intelligence can be used to automate the analytical process.
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Copyright
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