Big data in severe mental illness: the role of electronic monitoring tools and metabolomics

Reddy Rajula, Hema Sekhar

First

Writing - Original Draft Preparation

;Manchia, Mirko

Second

Writing - Original Draft Preparation

;Carpiniello, Bernardo

Penultimate

Writing - Review & Editing

;Fanos, Vassilios

Last

Writing - Review & Editing

2021-01-01

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Abstract

There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.