Concept-Level Local Explanations of Kidney Transplant Survival Predictions by Black-Box ML Models
Bibliographic record
Abstract
Existing explainable AI (XAI) methods, when applied to kidney transplant outcome prediction models, typically provide feature importance as opposed to a clinical concept-level description of how a prediction was generated from the input data. In this paper, we propose a novel XAI framework that provides explanations at the clinical concept level. Our framework first generates local explanations in the form of feature-level decision paths—i.e. sequences of conditions, represented as feature-value pairs, leading to a prediction that explain the models’ decision-making process based on input features. These decision paths are then translated into higher-level clinical concepts relevant to kidney transplantation. We use large language models (LLMs) enhanced with nephrology-specific knowledge and authoritative clinical guidelines (e.g., KDIGO standards) to generate clinically actionable concepts from low-level input features. The concepts are cross-validated across multiple LLM instances to enhance the clinical validity of the feature-to-concept mappings, and a systematic mapping approach using propositional logic and threshold-based rules is employed to balance expressiveness and simplicity of the mappings. The approach represents a step forward in integrating advanced AI systems with real-world clinical practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".