Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy

Kathleen D. Askland*, Sarah Garnaat, Nicholas J. Sibrava, Christina Lynn Boisseau, David Strong, Maria Mancebo, Benjamin Greenberg, Steve Rasmussen, Jane Eisen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)156-169
Number of pages14
JournalInternational Journal of Methods in Psychiatric Research
Volume24
Issue number2
DOIs
StatePublished - Jun 1 2015

Keywords

  • Obsessive compulsive disorder
  • Risk factors
  • Statistics

ASJC Scopus subject areas

  • Psychiatry and Mental health

Fingerprint Dive into the research topics of 'Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy'. Together they form a unique fingerprint.

Cite this