Prioritization of Multi-level Risk Factors, and Predicting Changes in Depression Ratings after Treatment Using Multi-Task Learning
Bibliographic record
Abstract
Major depressive disorder (MDD) is the most common mental health disorder and is one of the leading preventable causes of death in the United States (U.S.). It is also recognized as a global problem by the World Health organization (WHO). The persistence of MDD leads to many negative consequences including suicide and disability. The Hamilton Rating Scale for Depression (HAM-D) evaluates depression severity based on 17 risk factors (symptoms) of depression. Risk factor analysis is a process to identify and understand the risk factors contributing to a particular disease, and is an essential component in the development of efficient and effective prevention and intervention efforts. Most existing methods use a one-size-fits-all model to identify the risk factors at the population-level. However, this type of method fails to account for data heterogeneity within a population. To overcome this limitation, we formulate a subpopulation specific MDD risk factors (symptoms) ranking problem, under the framework of multi-task learning (MTL), to identify a ranked list of MDD risk factors for each subpopulation (task) simultaneously while utilizing appropriate shared information across tasks. By synchronously learning multiple related tasks, MTL provides a paradigm to rank risk factors both at the subpopulation and population-level. To the best of our knowledge, this is the first study to investigate HAM-D using MTL.
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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 | 0.008 |
| 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) | 0.001 | 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".