ArticleNPJ schizophrenia2021
Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach.
Article in NPJ schizophrenia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- Application of artificial intelligence and psychosocial functioning in psychosis: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Coping Style as a Predictor of Relapse and Functioning in First Episode Psychosis.Schizophrenia bulletin · 2026Article
- Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.JMIR mental health · 2026Observational
- Biomarkers for Predicting Clinical Deterioration in Schizophrenia-Spectrum Disorders: A Systematic Review.Brain sciences · 2026Review
- Delivery and delay of guideline pharmacological, psychological, and social interventions for adults with complex psychosis in Dutch inpatient rehabilitation units: A retrospective study.The International journal of social psychiatry · 2026Article
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- Neurocognition as a major predictor of 8-week response to antipsychotics for drug-naïve first-episode schizophrenia using machine learning.Schizophrenia (Heidelberg, Germany) · 2025Article
- The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.International journal of mental health nursing · 2025Review
- Investigating inequalities in patient outcomes for first-episode psychosis.The British journal of psychiatry : the journal of mental science · 2024Observational
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- 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
- Adverse outcome analysis in people at clinical high risk for psychosis: results from a 2-year Italian follow-up study.Social psychiatry and psychiatric epidemiology · 2024Article
- Recent onset mental illness severity: pilot study on the role of cognition, sensory modulation, and daily life participation.Frontiers in psychiatry · 2024Article
- Investigation of social and cognitive predictors in non-transition ultra-high-risk' individuals for psychosis using spiking neural networks.Schizophrenia (Heidelberg, Germany) · 2023Article
- Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.Current psychiatry reports · 2022Review
- Predictive models for social functioning in healthy young adults: A machine learning study integrating neuroanatomical, cognitive, and behavioral data.Social neuroscience · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Schizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF ≥ 65) and poor (GAF < 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study samples was estimated using leave-one-site-out cross-validation. Prediction accuracies were above chance and ranged from 62.2% to 64.7% (symptomatic outcome), and 63.5-67.6% (global outcome). Leave-one-site-out cross-validation demonstrated the robustness of our models, with a minor drop in predictive accuracies of 2.3% on average. Important predictors included GAF scores, psychotic symptoms, quality of life, antipsychotics use, psychosocial needs, and depressive symptoms. These robust, albeit modestly accurate, long-term prognostic predictions based on lean predictor sets indicate the potential of machine learning models complementing clinical judgment and decision-making. Future model development may benefit from studies scoping patient's and clinicians' needs in prognostication.
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Registered trials
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