Developing Contextual Ontology for Chronic Diseases: AI-Enhanced Extension and Prediction in an Asthma Case Study
Bibliographic record
Abstract
The growing complexity and interdependence of healthcare data, especially for chronic diseases such as asthma, demand innovative approaches for effective knowledge representation. This study introduces a general contextual ontology model for chronic diseases, extended specifically to asthma. Leveraging real-world datasets, the extended asthma ontology integrates key factors such as symptoms, triggers, treatments, and patient demographics, providing a comprehensive framework for disease management. The ontology was validated using intrinsic metrics such as classification, reusability, and completeness in healthcare applications. To validate the ontology, we used decision trees to extract rules after identifying the most relevant parameters needed to generate a Semantic Web Rule Language. These rules facilitate reasoning, validation, and decision-making within the ontology. The results highlight the potential of developing a general contextual ontology and extending it to address specific chronic diseases, such as asthma. We designed a general contextual ontology framework by integrating the extended ontology with artificial intelligence algorithms, identifying relevant parameters, and extracting rules to enhance knowledge representation and support clinical decision-making. This framework can be applied to other disease case studies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".