Machine learning for genetic prediction of psychiatric disorders: a systematic review.

ABSTRACT: Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts w

SUBMITTER: Bracher-Smith M 

PROVIDER: S-EPMC7610853 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature