Machine Learning in Obsessive-Compulsive Disorder Medications

Abstract

Obsessive-Compulsive Disorder (OCD) is a chronic and often debilitating psychiatric condition characterized by intrusive thoughts and repetitive behaviors. Although pharmacological treatments, particularly selective serotonin reuptake inhibitors (SSRIs), are commonly prescribed, response rates vary significantly among patients, and trial-and-error prescribing remains a major clinical challenge. Recent advances in machine learning (ML) offer promising tools to personalize and optimize treatment strategies for OCD. This review explores the application of ML techniques in the context of OCD medications, focusing on predictive modeling of treatment response, side effect profiling, and drug repurposing. Supervised learning algorithms have demonstrated potential in identifying biomarkers and clinical features that predict individual responses to SSRIs and augmenting agents. Additionally, unsupervised learning methods have been utilized to discover subtypes of OCD that may benefit from distinct pharmacological approaches. Despite these advances, challenges such as data heterogeneity, small sample sizes, and the need for interpretability remain. Integrating ML into clinical decision-making could pave the way for precision psychiatry in OCD, enabling more effective, faster, and safer treatment regimens.