Predicting 3-year persistent or recurrent major depressive episode using machine learning techniques
Bibliographic record
Abstract
The identification of predictors of recurrence and persistence of depressive episodes in major depressive disorder (MDD) can be important to inform clinicians and collaborate to clinical decisions. The aim of the present study is to predict recurrent or persistent depressive episodes, in addition to predicting severe recurrent or persistent depressive episodes using a machine learning method. This is a prospective cohort study with three years of follow-up. Individuals diagnosed with MDD in the first phase of the study (2012–2015) were evaluated in the second phase (2012–2015). The sociodemographic, clinical, comorbid disorders and substance use variables were used as predictors in all predictive models. Initially, the first model predicted recurrence/persistence, including subjects of any severity of depression level. The second model predicted recurrence/persistence depression as the first model, although it was trained with severely depressed subjects and those without indicative for depression. The third model predicted severe depression among depressed patients. Area under the curve (AUC) values ranged from 0.65 to 0.81, and accuracies ranged from 62% to 71%. Psychiatric comorbidities, substance abuse/dependence, and family medical history were important features in all three models. The time between baseline and the second phase of the study was approximately three years, making it difficult to detect depressive symptoms during this time frame. Also, age at depression onset and number of episodes were not included in the model due to the large number of missing data. In conclusion, this study adds new information that can help health professionals both in their clinical practice and in public services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".