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Cited by 75Obsessive-Compulsive Spectrum DisordersAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesMental Health Research Topics

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

Open access
Askland, Kathleen D., Garnaat, Sarah, Sibrava, Nicholas J., Boisseau, Christina L., Strong, David, Mancebo, Maria, Greenberg, Benjamin, Rasmussen, Steve +1 more·2015·International Journal of Methods in Psychiatric Research
DOI 10.1002/mpr.1463PMC PMC5466447
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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. Copyright © 2015 John Wiley & Sons, Ltd.

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  • SVM is a multivariate approach that does not rely on summary scores but considers every single item, reducing the risk of losing information.

    “Furthermore, it is a multivariate approach, as it does not rely on summary scores, but considers every single item. The risk of losing information is therefore reduced [41].”

    From Assessing ADHD symptoms in children and adults: evaluating the role of objective measures (2018) · cites this paper as [41]

    The Introduction says that reducing measures to summary scores can lose information, and the Discussion says that considering individual items avoids obscuring their effects. But the Discussion attributes this item-level approach to random forests; the paper mentions SVM in the Introduction without establishing that SVM has the claimed property. No page numbers are available in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • SVM is data-driven and less sensitive to outliers.

    “The advantage of the former is that it is data-driven and less sensitive to outliers [41].”

    From Assessing ADHD symptoms in children and adults: evaluating the role of objective measures (2018) · cites this paper as [41]

    The Introduction contrasts theory-driven parametric models, which it says are often sensitive to outliers, with machine-learning methods that can overcome some of those limitations. It identifies SVM as a machine-learning method, but does not specifically say that SVM is less sensitive to outliers. (LLM, conf=0.77)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 2

  1. Cleophas TJ 2013 Machine Learning in Medicine New York Springer
  2. First MB Spitzer RL Gibbon M Williams JBW 1996 Structured Clinical Interview for DSM-IV Axis I Disorders – Patient Edition (SCID/IP Version 2.;0) New York Biometrics Research Department, New York State Psychiatric Institute

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Authors
Askland, K. D., Garnaat, S., Sibrava, N. J., Boisseau, C. L., Strong, D., Mancebo, M., Greenberg, B., Rasmussen, S., & Eisen, J.
Affiliations
  • Brown University (US)
  • Baruch College (US)
  • University of California San Diego (US)
ORCID
Venue
International Journal of Methods in Psychiatric Research
Publisher
Wiley
Published
2015-05-21
Volume
24
Issue
2
Pages
156-169
ISSN
  • 1049-8931 (ISSN-L)
  • 1557-0657
DOI
10.1002/mpr.1463
PMC
PMC5466447
OpenAlex
W2115942934
Cited by
75
References
63
Citations by year
2026: 4 · 2025: 6 · 2024: 7 · 2023: 8 · 2022: 14 · 2021: 7 · 2020: 8 · 2019: 10 · 2018: 6 · 2017: 4 · 2016: 1
FWCI
4.52
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • International Journal of Methods in Psychiatric Research (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
National Institute of Mental Health (MH085810-05, MH091032, MH060218)
Topics
  • Social Sciences › Psychology › Clinical Psychology › Obsessive-Compulsive Spectrum Disorders
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords
obsessive-compulsive disorder, remission prediction, random forest, Yale-Brown Obsessive Compulsive Scale, social adaptation, clinical decision-making
MeSH terms
Machine Learning, Adult, Female, Humans, Longitudinal Studies, Male, Obsessive-Compulsive Disorder, Predictive Value of Tests, Psychiatric Status Rating Scales, Psychometrics, Recurrence, Retrospective Studies

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