ArticleNPJ schizophrenia2019
Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning.
Article in NPJ schizophrenia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 5 of them syntheses 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.
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Who cites it
48 citing papers in PubMed, 5 syntheses or guidelines pooled it, 121 citations in OpenAlex.
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled it
- Application of artificial intelligence and psychosocial functioning in psychosis: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.Schizophrenia bulletin · 2021Pooled it
- Resting-state brain age is associated with cognitive and sensorimotor abnormalities in schizophrenia spectrum disorders: a validation & longitudinal study.Translational psychiatry · 2026Article
- Mapping Functional Brain Organization Using Artificial Intelligence.Chemical & biomedical imaging · 2026Review
- Artificial Intelligence for Mental Health Monitoring: A Solution for Digital Behavioral Health Care and Education-An Umbrella Review.Health science reports · 2026Review
- Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia.Frontiers in psychiatry · 2025Article
- Artificial intelligence in systemic diagnostics: Applications in psychiatry, cardiology, dermatology and oral pathology.Bioinformation · 2025Article
- Unraveling multi-scale neuroimaging biomarkers and molecular foundations for schizophrenia: A combined multivariate pattern analysis and transcriptome-neuroimaging association study.CNS neuroscience & therapeutics · 2024Article
- Primary care physicians' perceptions of artificial intelligence systems in the care of adolescents' mental health.BMC primary care · 2024Article
- Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight.Risk management and healthcare policy · 2024Review
- Improving Psychiatry Services with Artificial Intelligence: Opportunities and Challenges.Turk psikiyatri dergisi = Turkish journal of psychiatry · 2024Review
- Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis.Frontiers in psychology · 2024Article
- Reinforcement learning deficits exhibited by postnatal PCP-treated rats enable deep neural network classification.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Article
- Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning.Neuroscience bulletin · 2023Review
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals.Cognitive neurodynamics · 2023Article
- Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review.Health science reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia (N = 81) as well as age- and sex-matched healthy controls (N = 93). We present an ensemble model -- EMPaSchiz (read as 'Emphasis'; standing for 'Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction') that stacks predictions from several 'single-source' models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples (N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.
Identifiers
What Socratic 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 Socratic graph.