Developing rank-based classifiers for hip fracture prediction
Bibliographic record
Abstract
This thesis presents a comprehensive study of the Maxima Nomination Sampling (MaxNS) method to construct rank-based classifiers for binary classification and their application. Utilizing the bone mineral density (BMD) dataset provided by the Manitoba BMD program, this research contrasts the efficacy of MaxNS methods with that of Simple Random Sampling (SRS) in handling data imbalance, a common challenge in medical data analysis. The study explores the process of data sampling using expert knowledge that can be represented in terms of rank based on the likelihood of a sampling unit coming from the underlying class of interest, as well as data pre-processing including feature scaling, to optimize the dataset for machine learning models. A significant emphasis is placed on the use of Deep Neural Networks (DNNs), specifically in processing hip Dual-energy X-ray Absorptiometry (DXA) images, to extract ranking information for MaxNS sampling. The findings demonstrate that MaxNS methods significantly outperform SRS, especially in terms of recall metrics, showcasing their robustness and reliability in predicting the minority class. This research contributes to the growing field of medical data analytics by providing insights into advanced sampling methods and their potential to improve the accuracy of predictive models in healthcare. The implications of this study extend to the development of more effective tools for medical diagnostics, ultimately aiming to enhance patient care through better predictive analytics.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".