Ontology Engineering to Model Clinical Pathways: Towards the Computerization and Execution of Clinical Pathways
Bibliographic record
Abstract
Clinical pathways translate evidence-based recommendations into locally practicable, process-specific algorithms that reduce practice variations and optimize quality of care. Our objective was to abstract practice-oriented knowledge from a cohort of real clinical pathways and represent this knowledge as a clinical pathway ontology. We employed a four step methodology: (1) knowledge source identification and classification of clinical pathways according to variations in setting, stage of care, patient type, outcome and specialty; (2) iterative knowledge abstraction using grounded theory; (3) ontology engineering as adapted from the Model-based Incremental Knowledge Engineering approach; and, (4) ontology evaluation through encoding a sample of real clinical pathways. We present our clinical pathway ontology that offers a detailed ontological model describing the structure and function of clinical pathways. Our ontology can potentially integrate with a healthcare semantic web, and ontologies for clinical practice guidelines, patients and institutions to form the foundational knowledge for generating patient-specific CarePlans.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".