ReviewTranslational psychiatry2023
Machine learning methods to predict outcomes of pharmacological treatment in psychosis.
Review in Translational psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Multivariate brain-behaviour associations in psychiatric disorders.Translational psychiatry · 2024Pooled it
- Schizophrenia: Converging Neurobiological Mechanisms and Emerging Therapeutic Strategies.Biomolecules · 2026Review
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Early prediction of periventricular leukomalacia from MRI changes: a machine learning approach for risk stratification.European radiology · 2026Article
- A conversational agent as a virtual therapist for patients diagnosed with schizophrenia: A preliminary study.PloS one · 2026Article
- Machine Learning Models for Predicting Antipsychotic Effectiveness and Separate Cost-Effectiveness Analysis in Hospitalized Schizophrenia Patients.Neuropsychiatric disease and treatment · 2026Article
- Developing and validating a machine learning pharmaceutical therapy recommender system for US-based hospital in-patients with schizophrenia spectrum disorders.BMC psychiatry · 2025Article
- Cerebellar-Cortical Connectivity and Prediction of Antipsychotic Treatment Response in First-Episode Psychosis.Schizophrenia bulletin · 2025Article
- Personalized Prediction of Clozapine Treatment Response Using Therapeutic Drug Monitoring Data in Japanese Patients with Treatment-Resistant Schizophrenia.Journal of clinical medicine · 2025Article
- Neural and somatic mechanisms driving clinical improvements in post-acute schizophrenia spectrum disorders.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- Salience Network Connectivity Predicts Response to Repetitive Transcranial Magnetic Stimulation in Smoking Cessation: A Preliminary Machine Learning Study.Brain connectivity · 2025Article
- Neurocognition as a major predictor of 8-week response to antipsychotics for drug-naïve first-episode schizophrenia using machine learning.Schizophrenia (Heidelberg, Germany) · 2025Article
- Prognostic predictions in psychosis: exploring the complementary role of machine learning models.BMJ mental health · 2025Article
- Predicting anorexia nervosa treatment efficacy: an explainable machine learning approach.Journal of eating disorders · 2025Article
- Advancing treatment response prediction in first-episode psychosis: integrating clinical and electroencephalography features.Psychiatry and clinical neurosciences · 2025Article
- A hybrid model for predicting response to risperidone after first-episode psychosis.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Article
- Simulated virtual reality experiences for predicting early treatment response in panic disorder.Frontiers in digital health · 2025Article
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review.JMIR bioinformatics and biotechnology · 2024Article
- Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN.Schizophrenia (Heidelberg, Germany) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.
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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.