ArticleEuropean child & adolescent psychiatry2015
Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model.
Article in European child & adolescent psychiatry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Transition of Substance-Induced, Brief, and Atypical Psychoses to Schizophrenia: A Systematic Review and Meta-analysis.Schizophrenia bulletin · 2020Pooled it
- Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders.NeuroImage. Clinical · 2015Pooled it
- Women with first-episode psychosis: diagnostic stability and clinical trajectories.Archives of women's mental health · 2026Observational
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Motor Behavior is Relevant for Understanding Mechanism, Bolstering Prediction, And Improving Treatment: A Transdiagnostic Perspective.Schizophrenia bulletin · 2022Article
- Abnormal regional homogeneity in right caudate as a potential neuroimaging biomarker for mild cognitive impairment: A resting-state fMRI study and support vector machine analysis.Frontiers in aging neuroscience · 2022Article
- Optimizing functional near-infrared spectroscopy (fNIRS) channels for schizophrenic identification during a verbal fluency task using metaheuristic algorithms.Frontiers in psychiatry · 2022Article
- Taming the chaos?! Using eXplainable Artificial Intelligence (XAI) to tackle the complexity in mental health research.European child & adolescent psychiatry · 2021Article
- Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification.International journal of environmental research and public health · 2021Review
- Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification.Diagnostics (Basel, Switzerland) · 2021Article
- Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis.Translational psychiatry · 2020Article
- Diagnosing schizophrenia with network analysis and a machine learning method.International journal of methods in psychiatric research · 2020Article
- Classification of Schizophrenia by Functional Connectivity Strength Using Functional Near Infrared Spectroscopy.Frontiers in neuroinformatics · 2020Article
- Machine Learning for Differential Diagnosis Between Clinical Conditions With Social Difficulty: Autism Spectrum Disorder, Early Psychosis, and Social Anxiety Disorder.Frontiers in psychiatry · 2020Article
- Neural correlates of binocular depth inversion illusion in antipsychotic-naïve first-episode schizophrenia patients.European archives of psychiatry and clinical neuroscience · 2019Article
- Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals.Schizophrenia bulletin · 2018Article
- Is It Possible to Predict the Future in First-Episode Psychosis?Frontiers in psychiatry · 2018Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challenging. To assess the diagnostic predictive value of multiple types of data at the emergence of early-onset first-episode psychosis (FEP), various support vector machine (SVM) classifiers were developed. The data were from a 2-year, prospective, longitudinal study of 81 patients (age 9-17 years) with early-onset FEP and a stable diagnosis during follow-up and 42 age- and sex-matched healthy controls (HC). The input was different combinations of baseline clinical, neuropsychological, magnetic resonance imaging brain volumetric and biochemical data, and the output was the diagnosis at follow-up (SSD vs. non-SSD, SSD vs. HC, and non-SSD vs. HC). Enhanced recursive feature elimination was performed for the SSD vs. non-SSD classifier to select and rank the input variables with the highest predictive value for a diagnostic outcome of SSD. After validation with a test set and considering all baseline variables together, the SSD vs. non-SSD, SSD vs. HC and non-SSD vs. HC classifiers achieved an accuracy of 0.81, 0.99 and 0.99, respectively. Regarding the SSD vs. non-SSD classifier, a combination of baseline clinical variables (severity of negative, disorganized symptoms and hallucinations or poor insight) and neuropsychological variables (impaired attention, motor coordination, and global cognition) showed the highest predictive value for a diagnostic outcome of SSD. Neuroimaging and biochemical variables at baseline did not add to the predictive value. Thus, comprehensive clinical/cognitive assessment remains the most reliable approach for differential diagnosis during early-onset FEP. SVMs may constitute promising multivariate tools in the search for predictors of diagnostic outcome in FEP.
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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.