Developing Contextual Ontology for Chronic Diseases: AI-Enhanced Extension and Prediction in an Asthma Case Study

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.