Predicting bipolar disorder incidence in young adults using gradient boosting: a 5-year follow-up study
Bibliographic record
Abstract
Abstract This study aimed to develop a classification model predicting incident bipolar disorder (BD) cases in young adults within a 5-year interval, using sociodemographic and clinical features from a large cohort study. We analyzed 1,091 individuals without BD, aged 18 to 24 years at baseline, and used the XGBoost algorithm with feature selection and oversampling methods. Forty-nine individuals (4.49%) received a BD diagnosis five years later. The best model had an acceptable performance (test AUC: 0.786, 95% CI: 0.686, 0.887) and included ten features: feeling of worthlessness, sadness, current depressive episode, selfreported stress, self-confidence, lifetime cocaine use, socioeconomic status, sex frequency, romantic relationship, and tachylalia. We performed a permutation test with 10,000 permutations that showed the AUC from the built model is significantly better than random classifiers. The results provide insights into BD as a latent phenomenon, as depression is its typical initial manifestation. Future studies could monitor subjects during other developmental stages and investigate risk populations to improve BD characterization. Furthermore, the usage of digital health data, biological, and neuropsychological information and also neuroimaging can help in the rise of new predictive models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".