title: Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy.
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.
authors.affiliation: Department of Psychiatry and Human Behavior, Butler Hospital/Warren Alpert School of Medicine, Brown University, Providence, RI, USA.
authors.forename: Kathleen D
authors.initials: KD
authors.lastname: Askland
authors.affiliation: Department of Psychiatry and Human Behavior, Butler Hospital/Warren Alpert School of Medicine, Brown University, Providence, RI, USA.
authors.forename: Sarah
authors.initials: S
authors.lastname: Garnaat
authors.affiliation: Department of Psychology, Baruch College - The City University of New York, New York, USA.
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Sibrava
authors.affiliation: Department of Psychiatry and Human Behavior, Butler Hospital/Warren Alpert School of Medicine, Brown University, Providence, RI, USA.
authors.forename: Christina L
authors.initials: CL
authors.lastname: Boisseau
authors.affiliation: Department of Family and Preventive Medicine, University of California, San Diego, CA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Strong
authors.affiliation: Department of Psychiatry and Human Behavior, Butler Hospital/Warren Alpert School of Medicine, Brown University, Providence, RI, USA.
authors.forename: Maria
authors.initials: M
authors.lastname: Mancebo
authors.affiliation: Department of Psychiatry and Human Behavior, Butler Hospital/Warren Alpert School of Medicine, Brown University, Providence, RI, USA.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Greenberg
authors.affiliation: Department of Psychiatry and Human Behavior, Butler Hospital/Warren Alpert School of Medicine, Brown University, Providence, RI, USA.
authors.forename: Steve
authors.initials: S
authors.lastname: Rasmussen
authors.affiliation: Department of Psychiatry and Human Behavior, Butler Hospital/Warren Alpert School of Medicine, Brown University, Providence, RI, USA.
authors.forename: Jane
authors.initials: J
authors.lastname: Eisen
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D000069550:Machine Learning; D008297:Male; D009771:Obsessive-Compulsive Disorder / Q000175:diagnosis*; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales; D011594:Psychometrics; D012008:Recurrence; D012189:Retrospective Studies
keywords: obsessive compulsive disorder; risk factors; statistics