Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".