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 prediction 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 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. General and Big Data features were extracted from the studies reviewed, and analyzed in the context of data collection, protection, storage and for what concerns data processing, targeted disorder and application purpose.

Results: A collection of 23 studies were analyzed, mostly targeting depression ($n=13$) and anxiety ($n=4$). For what concerns data sources, mostly social media posts ($n=5$), tweets ($n=7$), and medical records ($n=6$) were used. Various Big Data technologies were used: for data protection, only 7 studies faced the problem, with anonymization schemes for medical records and only surveys ($n=4$), and safe authentication methods for social media ($n=3$). For data processing, Machine Learning (ML) models appeared in 22 studies of which Random Forest (RF) was the most widely used ($n=5$). Logistic Regression (LR) was used in 4 studies, and Support Vector Machine (SVM) was used in 3 studies.

Conclusion: In order to utilize Big Data as a way to mitigate mental health disorders and predict their appearance a great effort is still needed. Integration and analysis of Big Data coming from different sources such as social media and health records and information exchange between multiple disciplines is also needed. Doctors and researchers alike can find patterns in otherwise difficult to identify data by making use of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Similarly, AI and ML can be used to automate the analytical process.