ArticleScientific reports2021
Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 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
- Role of emotional intelligence in schizophrenia: ability-based vs. self-reported assessment.European archives of psychiatry and clinical neuroscience · 2026Article
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?Schizophrenia (Heidelberg, Germany) · 2025Review
- Interpretable machine learning to evaluate relationships between DAO/DAOA (pLG72) protein data and features in clinical assessments, functional outcome, and cognitive function in schizophrenia patients.Schizophrenia (Heidelberg, Germany) · 2025Article
- Predicting adolescent psychopathology from early life factors: A machine learning tutorial.Global epidemiology · 2024Article
- 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
- Confining Pressure Forecasting of Shield Tunnel Lining Based on GRU Model and RNN Model.Sensors (Basel, Switzerland) · 2024Article
- Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review.Consortium psychiatricum · 2023Review
- Investigating White Matter Abnormalities Associated with Schizophrenia Using Deep Learning Model and Voxel-Based Morphometry.Brain sciences · 2023Article
- Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2.Sensors (Basel, Switzerland) · 2022Article
- Prediction of successful aging using ensemble machine learning algorithms.BMC medical informatics and decision making · 2022Article
- Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province.Frontiers in medicine · 2022Article
- Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.International journal of molecular sciences · 2021Review
- Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach.Journal of personalized medicine · 2021Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while functional outcome is assessed by the Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF) Scale. To determine the outcome assessed by QLS and GAF, we established a bagging ensemble framework with a feature selection algorithm resulting from the analysis of factors such as 3 clinical symptom scales and 11 cognitive function scores of 302 patients with schizophrenia in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, support vector machine, linear regression, and random forests. The analysis revealed that the bagging ensemble model with feature selection performed best among predictive models in predicting the QLS functional outcome by using 20-item Scale for the Assessment of Negative Symptoms (SANS20) and 17-item Hamilton Depression Rating Scale (HAMD17). Moreover, to predict the GAF outcome, the bagging ensemble model with feature selection performed best among predictive models by using SANS20 and the Positive and Negative Syndrome Scale-Positive (PANSS-Positive) subscale. The study indicates that there are synergistic effects between negative (SANS20) and depressive (HAMD17) symptoms as well as between negative and positive (PANSS-Positive) symptoms in influencing functional outcome of schizophrenia using the bagging ensemble framework with feature selection.
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