Screening for bipolar disorder in a tertiary mental health centre using EarlyDetect: A machine learning-based pilot study
Bibliographic record
Abstract
: Bipolar disorder (BD) is a prevalent mental health illness with a direct impact on patient's well-being. Self-report-based BD screening questionnaires such as the Mood Disorder Questionnaire (MDQ) is economical and clinically validated. We use a machine-learning approach to test whether utilizing our composite screening application - EarlyDetect (ED), designed for assessing an array of mental health illness, can enhance bipolar disorder screening over MDQ. : This was a retrospective, naturalistic study at a tertiary mental health centre in western Canada. Participants (n = 955; 56.4% female; mean age 35.4; 18.7% BD) completed ED and underwent a clinical interview with a blinded psychiatrist for diagnostic accuracy. Elastic net and leave-one-out cross-validation was used to make more confident predictions at an individual level. : Using composite scoring, the balanced accuracy of our tool was 80.6%, with a sensitivity of 73.7% and a specificity of 87.5%. Compared with the MDQ original scoring method, the fully composite ED model improved balanced accuracy by 6.9%, sensitivity by 14.5%, while maintaining specificity. : Patients were assessed using clinical psychiatric evaluations, which are subjective. There is also the potential for self-reporting bias. BD subtypes were not differentiated. The cross-sectional design of this study rules out conclusions of causality. : Our results show improved BD detection accuracy using composite measures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".