Big data in severe mental illness: the role of electronic monitoring tools and metabolomics.
accuracy
bipolar disorder
digital monitoring
machine learning
major depressive disorder
metabolite
personalized medicine
schizophrenia
Journal
Personalized medicine
ISSN: 1744-828X
Titre abrégé: Per Med
Pays: England
ID NLM: 101238549
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
pubmed:
31
10
2020
medline:
5
11
2021
entrez:
30
10
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33124507
doi: 10.2217/pme-2020-0033
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
75-90Subventions
Organisme : Marie Curie
ID : 721567
Pays : United Kingdom