Developing Contextual Ontology for Chronic Diseases: AI-Enhanced Extension and Prediction in an Asthma Case Study
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Biomedical Text Mining and Ontologies · Semantic Web and Ontologies · Data Quality and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer Science Department, Université du Québec à Chicoutimi, 555, Boul de l’Université, Chicoutimi, QC G7H 2B1, Canada
Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
Computer Science Department, Université Libanaise, Beirut 6573/14, Lebanon
References (79)
Show source-provided references
1. World Health Organization (WHO) (2025, January 26). Asthma Fact Sheet. Available online: https://www.who.int.
2. Global Initiative for Asthma (2025, January 26). Global Strategy for Asthma Management and Prevention. Available online: https://ginasthma.org.
3. Allergic asthma: An overview of triggers and management strategies
4. Description Logics as Ontology Languages for the Semantic Web
5. Advances techniques in difficult biliary cannulation
6. Msheik, B., Adda, M., and Mcheick, H. (2024). Survey on Knowledge Representation Models in Healthcare. Information, 15.10.3390/info15080435 ↗
7. A three-stage ontology-driven solution to provide personalized care to chronic patients at home10.1016/j.jbi.2013.03.006 ↗
8. Ensuring data standardization with controlled vocabularies: A big data challenge for healthcare
9. Holzinger, A., and Jurisica, I. (2014). Knowledge discovery and data mining in biomedical informatics. Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, Springer.10.1007/978-3-662-43968-5 ↗
10. McMahan, H.B., Moore, E., Ramage, D., and Hampson, S. (2017, January 20–22). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA.
11. Ajami, H., and Mcheick, H. (2018). Ontology-based model to support ubiquitous healthcare systems for COPD patients. Electronics, 7.10.3390/electronics7120371 ↗
12. Context Aware Computing for The Internet of Things: A Survey10.1109/SURV.2013.042313.00197 ↗
13. Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression, Wiley. [3rd ed.].10.1002/9781118548387 ↗
14. Systematized Nomenclature of Medicine-Clinical Terminology (SNOMED CT) Clinical Use Cases in the Context of Electronic Health Record Systems: Systematic Literature Review10.2196/43750 ↗
15. A translation approach to portable ontology specifications10.1006/knac.1993.1008 ↗
16. Ontologies for knowledge sharing
17. Engineering ontologies10.1006/ijhc.1996.0096 ↗
18. Knowledge engineering: Principles and methods10.1016/S0169-023X(97)00056-6 ↗
19. Maedche, A. (2002). Ontology Learning for the Semantic Web, Kluwer Academic Publishers.10.1007/978-1-4615-0925-7 ↗
20. Knowledge representation with ontologies: The present and future10.1109/MIS.2004.1265889 ↗
21. Cancer diagnosis and treatment using ontologies
22. Semantic ontology for EHR interoperability
23. Rare disease data integration using ontologies
24. Ontology-based diabetes management
25. Ontologies in drug discovery
26. Genomic-based precision medicine through ontologies
27. Ontology applications for chronic ill patients under treatment
28. Ontology-based representation of brain neoplasms
29. Ontology for health system modeling
30. Ontology in mental health and therapy planning
31. COVID-19 pandemic response through ontology-based frameworks
32. Complex healthcare data structures with ontologies
33. Ontology-based integration of health IoT systems
34. Frameworks for healthcare systems using ontologies
35. COVID-19 safety measures in healthcare with ontologies
36. Healthcare data privacy and epidemiology through ontologies
37. Patient-centric care with ontology-based approaches
38. Ontology applications in healthcare data preparation
39. BioPortal: Enhanced functionality via new Web services from the National Center for Biomedical Ontology10.1093/nar/gkr469 ↗
40. Bio-ontologies: Current trends and future directions10.1093/bib/bbl027 ↗
41. Formal ontologies in biomedical knowledge representation10.1055/s-0038-1638845 ↗
42. Ontology-based data integration in the health domain: Challenges, review, and a proposal
43. Rector, A.L. (2003, January 23–25). Modularisation of domain ontologies implemented in description logics and related formalisms including OWL. Proceedings of the International Conference on Knowledge Capture, Sanibel Island, FL, USA.10.1145/945645.945664 ↗
44. BioPortal as a dataset of linked biomedical ontologies and terminologies in RDF10.3233/SW-2012-0086 ↗
45. Baron, J.A., and Schriml, L.M. (2021, January 15–18). Challenges Arising from Ontology Imports Utilized for Exploring Mechanisms of Disease. Proceedings of the 12th International Conference on Biomedical Ontologies, Bolzano, Italy.
46. Vasilevsky, N.A., Matentzoglu, N.A., Toro, S., Flack IV, J.E., Hegde, H., Unni, D.R., and Haendel, M.A. (2022). Mondo: Unifying diseases for the world, by the world. MedRxiv.10.1101/2022.04.13.22273750 ↗
47. The Monarch Initiative in 2024: An analytic platform integrating phenotypes, genes and diseases across species10.1093/nar/gkad1082 ↗
48. Towards a Methodology for Ontology Development
49. Grüninger, M., and Fox, M.S. (1995, January 19–20). Methodology for the Design and Evaluation of Ontologies. Proceedings of the Workshop on Basic Ontological Issues in Knowledge Sharing, Montreal, QC, Canada.
50. Fernández-López, M., Gómez-Pérez, A., and Juristo, N. (1997, January 24–25). METHONTOLOGY: From Ontological Art Towards Ontological Engineering. Proceedings of the Spring Symposium on Ontological Engineering of AAAI, Palo Alto, CA, USA.
51. The Ontolingua Server: A Tool for Collaborative Ontology Construction10.1006/ijhc.1996.0121 ↗
52. Swartout, B., Patil, R., Knight, K., and Russ, T. (1996, January 9–14). Toward Distributed Use of Large-Scale Ontologies. Proceedings of the Tenth Workshop on Knowledge Acquisition for Knowledge-Based Systems, Banff, AB, Canada.
53. Noy, N.F., and McGuinness, D.L. (2001). Ontology Development 101: A Guide to Creating your First Ontology, Stanford Knowledge Systems Laboratory (KSL), Stanford University.
54. Methodological guidelines for reusing general ontologies10.1016/j.datak.2013.03.006 ↗
55. Mcheick, H., Saleh, L., Ajami, H., and Mili, H. (2017). Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network. Sens. J., 17.10.3390/s17071486 ↗
56. Staab, S., and Studer, R. (2004). Ontology Evaluation. Handbook on Ontologies. International Handbooks on Information Systems, Springer.10.1007/978-3-540-24750-0 ↗
57. Lovrenčić, S., and Čubrilo, M. (2008, January 24–26). Ontology evaluation—Comprising verification and validation. Proceedings of the Central European Conference on Information and Intelligent Systems (CECIIS 2008), Varaždin, Croatia.
58. Combining METHONTOLOGY and Cyc 101 for Medical Ontology Design
59. Jonathan, E. (2008). Requirements-Oriented Methodology for Evaluating Ontologies. [Ph.D. Thesis, RMIT University].
60. Gómez-Pérez, A., Fernández-López, M., and Corcho, O. (2004). Ontological Engineering: With Examples from the Areas of Knowledge Management, e-Commerce and the Semantic Web, Springer.
61. Recognition of a familiar place by the honeybee (Apis mellifera)10.1007/s00359-004-0592-6 ↗
62. Decision Trees10.1023/A:1010933404324 ↗
63. Rokach, L. (2014). Data Mining with Decision Trees: Theory and Applications, World Scientific Publishing Co. Pte. Ltd.10.1142/9097 ↗
64. Classification and Regression Trees10.1002/widm.8 ↗
65. Kleinbaum, D.G., and Klein, M. (2010). Logistic Regression: A Self-Learning Text, Springer. [3rd ed.].10.1007/978-1-4419-1742-3 ↗
66. Mekkaoui, S.E., Benabbou, L., and Berrado, A. (2023). Rule-Extraction Methods From Feedforward Neural Networks: A Systematic Literature Review. arXiv.
67. Gallucci, M., Carbonara, P., Pacilli, A.M.G., di Palmo, E., Ricci, G., and Nava, S. (2019). Use of symptoms scores, spirometry, and other pulmonary function testing for asthma monitoring. Front. Pediatr., 7.10.3389/fped.2019.00054 ↗
68. World Health Organization (2000). Obesity: Preventing and Managing the Global Epidemic, World Health Organization. WHO Technical Report Series 894.
69. Hayashi, Y., and Takano, N. (2020). One-dimensional convolutional neural networks with feature selection for highly concise rule extraction from credit scoring datasets with heterogeneous attributes. Electronics, 9.10.3390/electronics9081318 ↗
70. An Introduction to Variable and Feature Selection
71. SWRL: A Semantic Web Rule Language Combining OWL and RuleML
72. Rule-Based Reasoning in Ontology-Driven Systems: Applications and Advancements
73. Ontology Reasoning in Healthcare
74. OWL Reasoning Techniques
75. Sadoughi, F., Behmanesh, A., and Sayfouri, N. (2020). Ontology-Based Clinical Decision Support Systems. J. Biomed. Inform., 103.10.1016/j.jbi.2020.103383 ↗
76. Enhancing Patient Outcomes with Ontology-Driven Systems
77. Ontology Validation in Complex Medical Contexts
78. Nguyen, T.H., and Tettamanzi, A.G.B. (2019, January 14–17). An evolutionary approach to class disjointness axiom discovery. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Thessaloniki, Greece.10.1145/3350546.3352502 ↗
79. Yemson, R., Kabir, S., Thakker, D., and Konur, S. (2023). Ontology development for detecting complex events in stream processing: Use case of air quality monitoring. Computers, 12.10.3390/computers12110238 ↗