Development and External Validation of a Treatment-Adjusted Machine Learning Model for Precision Allocation of Group-Based Depression Care Among People Living with HIV in Uganda.
Bibliographic record
Abstract
Background: Group-based depression care is widely used in HIV services in Uganda, yet some patients remain symptomatic following treatment. We developed and externally validated a treatment-adjusted machine learning model to support risk-informed group-based depression care for people living with HIV(PLWH). Methods: We analyzed data from 1,140 adults living with HIV and significant depression symptoms enrolled across 30 HIV clinics in the SEEK-GSP trial (PACTR201608001738234). Participants received either Group Support Psychotherapy (GSP) or Group HIV Education (GHE). The primary outcome was six-month depression non-remission, defined as Self-Reporting Questionnaire (SRQ) score ≥ 6 and a functional impairment score < 9. Three machine learning models (Elastic Net, Random Forest, and XGBoost) were trained on baseline data from Gulu and Kitgum and externally validated in Pader district data. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration slope, intercept, Brier score and decision curve analysis. Sensitivity analyses excluding treatment assignment were conducted to assess the predictive value of baseline characteristics alone. Results: Treatment-adjusted models consistently outperformed treatment-excluded models. In external validation, the parsimonious XGBoost model showed the best overall performance (AUC 0.947), compared with Elastic Net (0.924) and Random Forest (0.918), and demonstrated clinical net benefit across relevant decision thresholds. Following Platt-recalibration, XGBoost showed the best overall performance, preserving strong discrimination (AUC 0.940) while improving the Brier score from 0.174 to 0.113 and the calibration slope from 7.533 to 1.093. Treatment assignment was the dominant predictor of depression non-remission risk, while age, HIV-related stigma, acceptance coping, socioeconomic vulnerability, low social support, and trauma-related symptoms also contributed to prediction. Conclusion: The externally validated, Platt-recalibrated parsimonious treatment-adjusted XGBoost model provides a promising approach to identifying people living with HIV at increased risk of depression non-remission and informing enhanced care following group-based depression treatment.
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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 | 0.033 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".