ArticleSchizophrenia bulletin2022
Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning.
Article in Schizophrenia bulletin, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- Coping Style as a Predictor of Relapse and Functioning in First Episode Psychosis.Schizophrenia bulletin · 2026Article
- Article
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Machine Learning Models for Predicting Antipsychotic Effectiveness and Separate Cost-Effectiveness Analysis in Hospitalized Schizophrenia Patients.Neuropsychiatric disease and treatment · 2026Article
- Ensuring generalizability and clinical utility in mental health care applications: Robust artificial intelligence-based treatment predictions in diverse psychosis populations.Psychiatry and clinical neurosciences · 2026Article
- Personalized Prediction of Clozapine Treatment Response Using Therapeutic Drug Monitoring Data in Japanese Patients with Treatment-Resistant Schizophrenia.Journal of clinical medicine · 2025Article
- Functional Coupling and Longitudinal Outcome Prediction in First-Episode Psychosis.Biological psychiatry global open science · 2025Article
- Review
- Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN.Schizophrenia (Heidelberg, Germany) · 2024Article
- Shaping tomorrow's support: baseline clinical characteristics predict later social functioning and quality of life in schizophrenia spectrum disorder.Social psychiatry and psychiatric epidemiology · 2024Article
- Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review.Brain sciences · 2024Article
- Diminished social motivation in early psychosis is associated with polygenic liability for low vitamin D.Translational psychiatry · 2024Article
- Role of Clinical Insight at First Month in Predicting Relapse at the Year in First Episode of Psychosis (FEP) Patients.Journal of clinical medicine · 2023Article
- Machine learning methods to predict outcomes of pharmacological treatment in psychosis.Translational psychiatry · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
32 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundValidated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis.
methodBaseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was further tested to predict 6-week remission in a sample of two independent, consecutive Danish first-episode cohorts.
resultsOf the 179 participants in Athens, 120 were male with an average age of 25.8 years and average duration of untreated psychosis of 32.8 weeks. 62.9% were antipsychotic-naïve. Fifty-seven percent attained remission after 4 weeks. In the Danish cohort, 31% attained remission. Eleven clinical scale items were selected in the Athens 4-week remission cohort. These included the Duration of Untreated Psychosis, Personal and Social Performance Scale, Global Assessment of Functioning and eight items from the Positive and Negative Syndrome Scale. This model significantly predicted 4-week remission status (area under the receiver operator characteristic curve (ROC-AUC) = 71.45, P < .0001). It also predicted 6-week remission status in the Danish cohort (ROC-AUC = 67.74, P < .0001), demonstrating reliability.
conclusionsUsing items from common and validated clinical scales, our model significantly predicted early remission in patients with first-episode psychosis. Although replicated in an independent cohort, forward testing between machine learning models and clinicians' assessment should be undertaken to evaluate the possible utility as a routine clinical tool.
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