Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease.

Objective: Individuals with immune-mediated inflammatory disease (IMID) have a higher prevalence of psychiatric disorders than the general population. We utilized machine-learning to identify patient-reported outcome measures (PROMs) that accurately predict major depressive disorder (MDD) and anxiety disorder in an IMID population.

Methods: Participants with IMID were enrolled in a cohort study and completed a Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID), and multiple PROMs. PROM items were ranked separately for MDD and anxiety disorder by the standardized mean difference between individuals with and without psychiatric disorders. Items were added sequentially to logistic regression (LR), neural network (NN), and random forest (RF) models. Discriminative performance was assessed with area under the receiver operator curve (AUC) and calibration was assessed with Brier scores. Ten-fold cross-validation was used.

Results: Of 637 participants, 75% were female and average age was 51 years. AUC and Brier scores respectively ranged from 0.87-0.91 and 0.07 (i.e., no variation) for MDD models, and from 0.79-0.83 and 0.09-0.11 for anxiety disorder models. In LR and NN, few PROM items were required to obtain optimal discriminatory performance. RF did not perform as well as LR and NN when few PROM items were included.

Conclusions: Predictive model performance was respectable and revealed insight into PROM items that are predictive of MDD and anxiety disorder. Models that included only the items 'I felt depressed' and 'I felt like I needed help for my anxiety' performed similarly to models that included all items from multiple PROMs.