ArticleInternational journal of methods in psychiatric research2015
Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy.
Article in International journal of methods in psychiatric research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.International journal of methods in psychiatric research · 2018Trial
- Evaluating clinical and neuroimaging predictors for cognitive-behavioral therapy outcome in obsessive-compulsive disorder.Scientific reports · 2026Article
- Characterizing and predicting refractory rumination in obsessive compulsive disorder.Journal of affective disorders · 2026Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures.Journal of clinical medicine · 2025Review
- The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.International journal of mental health nursing · 2025Review
- Predicting OCD severity from religiosity and personality: A machine learning and neural network approach.Journal of mood and anxiety disorders · 2024Article
- Examining the Most Important Risk Factors for Predicting Youth Persistent and Distressing Psychotic-Like Experiences.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2024Article
- Early detection of paediatric and adolescent obsessive-compulsive, separation anxiety and attention deficit hyperactivity disorder using machine learning algorithms.Health information science and systems · 2023Article
- Predicting Responses to Electroconvulsive Therapy in Adolescents with Treatment-Refractory Depression Based on Resting-State fMRI.Journal of clinical medicine · 2023Article
- Developing Transdiagnostic Internalizing Disorder Prognostic Indices for Outpatient Cognitive Behavioral Therapy.Behavior therapy · 2023Article
- Personality traits as predictors of OCD remission: A longitudinal study.Journal of affective disorders · 2023Observational
- Psychotherapies and digital interventions for OCD in adults: What do we know, what do we need still to explore?Comprehensive psychiatry · 2023Review
- Predicting the Properties of High-Performance Epoxy Resin by Machine Learning Using Molecular Dynamics Simulations.Nanomaterials (Basel, Switzerland) · 2022Article
- Computational Predictions for OCD Pathophysiology and Treatment: A Review.Frontiers in psychiatry · 2021Review
- Genomics of Obsessive-Compulsive Disorder-Toward Personalized Medicine in the Era of Big Data.Frontiers in pediatrics · 2021Review
- Use of machine learning to classify adult ADHD and other conditions based on the Conners' Adult ADHD Rating Scales.Scientific reports · 2020Article
- Supervised Machine Learning: A Brief Primer.Behavior therapy · 2020Review
- Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach.BMC psychiatry · 2020Article
- General personality dimensions, impairment and treatment response in obsessive-compulsive disorder.Personality and mental health · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The study objective was to apply machine learning methodologies to identify predictors of remission in a longitudinal sample of 296 adults with a primary diagnosis of obsessive compulsive disorder (OCD). Random Forests is an ensemble machine learning algorithm that has been successfully applied to large-scale data analysis across vast biomedical disciplines, though rarely in psychiatric research or for application to longitudinal data. When provided with 795 raw and composite scores primarily from baseline measures, Random Forest regression prediction explained 50.8% (5000-run average, 95% bootstrap confidence interval [CI]: 50.3-51.3%) of the variance in proportion of time spent remitted. Machine performance improved when only the most predictive 24 items were used in a reduced analysis. Consistently high-ranked predictors of longitudinal remission included Yale-Brown Obsessive Compulsive Scale (Y-BOCS) items, NEO items and subscale scores, Y-BOCS symptom checklist cleaning/washing compulsion score, and several self-report items from social adjustment scales. Random Forest classification was able to distinguish participants according to binary remission outcomes with an error rate of 24.6% (95% bootstrap CI: 22.9-26.2%). Our results suggest that clinically-useful prediction of remission may not require an extensive battery of measures. Rather, a small set of assessment items may efficiently distinguish high- and lower-risk patients and inform clinical decision-making.
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