ArticleTranslational psychiatry2020
A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data.
Article in Translational psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Article
- Improved schizophrenia detection using structural MRI with multi-scale transform and deep orthogonal features.Physical and engineering sciences in medicine · 2026Article
- Article
- Structured missingness in biomedical research.Communications AI & computing · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Functional Coupling and Longitudinal Outcome Prediction in First-Episode Psychosis.Biological psychiatry global open science · 2025Article
- Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19.European archives of psychiatry and clinical neuroscience · 2025Observational
- Advancing treatment response prediction in first-episode psychosis: integrating clinical and electroencephalography features.Psychiatry and clinical neurosciences · 2025Article
- From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.Biomedicines · 2025Review
- A hybrid model for predicting response to risperidone after first-episode psychosis.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Article
- Learning Latent Profiles via Cognitive Growth Charting in Psychosis: Design and Rationale for the PRECOGNITION Project.Schizophrenia bulletin open · 2025Article
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review.Brain sciences · 2024Article
- Clustering of antipsychotic-naïve patients with schizophrenia based on functional connectivity from resting-state electroencephalography.European archives of psychiatry and clinical neuroscience · 2023Article
- Artificial intelligence for dementia prevention.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia.Schizophrenia bulletin · 2023Article
- A framework for precision "dosing" of mental healthcare services: algorithm development and clinical pilot.International journal of mental health systems · 2023Article
- Dopamine Synthesis Capacity and GABA and Glutamate Levels Separate Antipsychotic-Naïve Patients With First-Episode Psychosis From Healthy Control Subjects in a Multimodal Prediction Model.Biological psychiatry global open science · 2023Article
- Psychotic disorders as a framework for precision psychiatry.Nature reviews. Neurology · 2023Review
- Machine learning methods to predict outcomes of pharmacological treatment in psychosis.Translational psychiatry · 2023Review
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Authors and funding
19 authors.
Funding
Abstract
The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.
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