Bibliographic record
Abstract
Machine learning models have the potential to transform healthcare by enabling the construction of decision support systems.However, a major challenge is the lack of transparency and accountability, as many models do not provide understandable explanations for their recommendations.Explainable Artificial Intelligence (XAI) methods aim to address this challenge by constructing and communicating explanations of how a model works and why it produces a particular output.This can help users evaluate the system and build trust in it, where appropriate.In this paper, we propose a method for explaining the relative rankings of predictions made by an XGBoost model, which involves understanding and comparing multiple predictions together.Our method uses counterfactual examples to show how changing the feature values of an entity can affect its position within the ranking defined by the model.Unlike traditional counterfactual explanations, which aim to find feature value changes that would result in a different predicted class label by meeting a fixed threshold, the proposed approach is unique in that it aims to identify changes that would bring the predictions in line with a dynamic threshold determined by other data items.We demonstrate the effectiveness of our approach in a healthcare triage problem.Our framework for counterfactual explanation provides a powerful tool for understanding the relationships between feature values and model rankings and can help promote transparency and accountability in healthcare decision-making and decision support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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; both teacher heads agree on what is shown here.
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".