Ontology Driven CPG Authoring and Execution via a Semantic Web Framework
Bibliographic record
Abstract
Clinical Practice Guidelines (CPG) are used by healthcare practitioners to standardize clinical practice and to provide evidence mediated health-care. Currently, there have been considerable efforts to computerize CPG so as to operationalize them within Clinical Decision Support Systems (CDSS) and to deploy them at the point of care. In our work, we take a semantic web approach - employing a domain ontology, a patient ontology, decision rules and a rule execution engine - towards the computerization and execution of CPG for CDSS. We present an ontology-driven approach for computerizing CPG and executing them based on individual patient instances. In our work we extend the Guideline Element Model (GEM) for computerizing CPG. We have (i) defined a CPG ontology based on the Document Type Definition (DTD) of GEM for ontologically representing a GEM encoded CPG; (ii) developed CPG decision logic definition tool and defined CPG rule syntax that allows practitioners to abstract and define decision logic rules based on the CPGs decision-variables inherent within the CPG; (iii) developed a forward-chaining CPG execution engine that executes the set of CPG execution logic rules using the JENA reasoning system; and (iv) implemented an automated justification tree generation module that provides the inference trace for the solution in order to assist practitioners in understanding the rationale for the proposed recommendations. In practice, given a patient instance our CDSS is able to derive CPG based clinical recommendations. We will present a working prototype of our CPG-based CDSS for the EU Radiation Protection 118 Referral Guideline for Imaging (RPG).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".