ArticleSchizophrenia research2022
Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank.
Article in Schizophrenia research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Application of artificial intelligence and psychosocial functioning in psychosis: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Metabolic biomarkers of treatment-resistant schizophrenia and cognitive correlates: insights from a machine learning.Molecular psychiatry · 2026Article
- Capturing multi-disease states on a spectrum with machine learning and routine clinical data.Med (New York, N.Y.) · 2026Article
- The role of age in the relationship between brain structure and cognition: moderator or confound?Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- The determination of human creative thinking by employing machine learning classification on EEG signals.Frontiers in psychology · 2026Article
- Ferroptosis Signature Correlates with Ovarian Cancer Prognosis and Chemotherapy Response.International journal of general medicine · 2026Article
- Machine learning helps predict early onset psychosis with serum protein biomarkers, neuropsychometry, and clinicodemographic data.Scientific reports · 2025Article
- What machine learning teaches us about depression prediction across the life course: An exploratory comparison of predictive models.SSM - population health · 2025Article
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review.JMIR bioinformatics and biotechnology · 2024Article
- Using machine learning model for predicting risk of memory decline: A cross sectional study.Heliyon · 2024Article
- Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction.Journal of integrative bioinformatics · 2024Article
- External Validation of a Machine Learning Model for Schizophrenia Classification.Journal of clinical medicine · 2024Article
- Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review.Consortium psychiatricum · 2023Review
- Article
- Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.ArXiv · 2023Article
- Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis.PLoS computational biology · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
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
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.