Reducing Sample Selection Bias in Clinical Data through Generation of Multi-Objective Synthetic Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the era of data-driven healthcare, identifying, quantifying, and mitigating bias in machine learning is of paramount importance.The impact of fair machine learning is particularly significant when predictions are applied in a clinical setting, where biased predictions can lead to unequal healthcare outcomes.In this paper, we consider the area of biomedical informatics and examine existing bias metrics and introduce a new metric to analyze bias in a smart home dataset.We investigate bias that may occur along sensitive attributes and examine its impact on the machine learning task of activity recognition from the collected data.In a novel approach to bias mitigation, we introduce a multi-objective generative adversarial network that creates synthetic data to mitigate sample bias by enhancing data diversity.We validate these methods using data collected for older adults living in smart homes who are managing multiple chronic health conditions, highlighting the potential of our approach to improve health predictions and outcomes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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