Distributionally Robust Optimization methods on robust medical diagnosis systems
Why this work is in the frame
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Bibliographic record
Abstract
In the medical field, modern recommendation systems face significant challenges due to distributional shifts in data. We propose utilizing Distributionally Robust Optimization (DRO) and Distributionally and Outlier Robust Optimization (DORO) methods to address this issue. This paper aims to develop suitable DRO and DORO frameworks for the medical domain and validate their effectiveness through extensive experiments. We employ the DDXPlus dataset for our investigations and cluster patients based on age, sex, and initial evidence to partition the data into distinct distributions. Using a simple three-layer neural network, we incorporate CVaR and CHISQ as DRO methods and their respective DORO forms. The experimental results show that the overall DRO approach demonstrates more significant enhancements while all four methods exhibit improvements over the original distributional scenarios. Our research contributes to optimizing deep learning models in the medical domain and enhancing their robustness. Furthermore, we intend to use these methods to estimate and provide best-fit patient therapies, addressing real-world medical challenges. The application of these approaches has the potential to enhance the performance and practicality of medical recommendation systems, offering improved medical services to patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it