Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank.
Machine learning
Polygenic risk scores
Precision psychiatry
Schizophrenia
Journal
Schizophrenia research
ISSN: 1573-2509
Titre abrégé: Schizophr Res
Pays: Netherlands
ID NLM: 8804207
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
01
04
2022
revised:
01
06
2022
accepted:
11
06
2022
pubmed:
3
7
2022
medline:
19
8
2022
entrez:
2
7
2022
Statut:
ppublish
Résumé
Machine learning (ML) holds promise for precision psychiatry, but its predictive performance is unclear. We assessed whether ML provided added value over logistic regression for prediction of schizophrenia, and compared models built using polygenic risk scores (PRS) or clinical/demographic factors. LASSO and ridge-penalised logistic regression, support vector machines (SVM), random forests, boosting, neural networks and stacked models were trained to predict schizophrenia, using PRS for schizophrenia (PRS
Identifiants
pubmed: 35779327
pii: S0920-9964(22)00240-7
doi: 10.1016/j.schres.2022.06.006
pmc: PMC9399753
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
156-164Subventions
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/T04604X/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : UKDRI-3003
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/L023784/2
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/L010305/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Informations de copyright
Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.
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