Clinical Practice Guidelines and Comorbid Diseases: A MiniZinc Representation of Guideline Models for Mitigating Adverse Interactions
Bibliographic record
Abstract
Managing a patient with comorbid diseases according to multiple clinical practice guidelines (CPGs) may result in adverse interactions that need to be mitigated (identified and addressed) so a safe therapy can be devised. However, mitigation poses both clinical and methodological challenges. It requires extensive domain knowledge and calls for advanced CPG models and efficient algorithms to process them. We respond to the above challenges by describing our algorithm that mitigates interactions between pairs of CPGs. The algorithm creates logical models of analyzed CPGs and uses constraint logic programming (CLP) together with domain knowledge, codified as interaction and revision operators, to process them. Logical CPG models are transformed into CLP-CPG models that are solved to find a safe therapy. We represent these CLP-CPG models using MiniZinc, a standard language for CLP models. As motivation and illustration of our mitigation algorithm we use a clinical case study describing a patient managed for hypertension and deep vein thrombosis according to two individual CPGs. We apply the algorithm to this scenario and present MiniZinc representations of the constructed CLP-CPG models.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".