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Design of a Pneumonia Ontology for Diagnosis Decision Support System

Authors: Sabrina Azzi, Michal Iglewski, Véronique Nabelsi


Diagnosis error problem is frequent and one of the most important safety problems today. One of the main objectives of our work is to propose an ontological representation that takes into account the diagnostic criteria in order to improve the diagnostic. We choose pneumonia disease since it is one of the frequent diseases affected by diagnosis errors and have harmful effects on patients. To achieve our aim, we use a semi-automated method to integrate diverse knowledge sources that include publically available pneumonia disease guidelines from international repositories, biomedical ontologies and electronic health records. We follow the principles of the Open Biomedical Ontologies (OBO) Foundry. The resulting ontology covers symptoms and signs, all the types of pneumonia, antecedents, pathogens, and diagnostic testing. The first evaluation results show that most of the terms are covered by the ontology. This work is still in progress and represents a first and major step toward a development of a diagnosis decision support system for pneumonia.

Keywords: Clinical decision support system, diagnostic errors, ontology, pneumonia.

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[1] S. Holmboe Eric and J. Durning Steven, "Assessing clinical reasoning: moving from in vitro to in vivo," in Diagnosis vol. 1, ed, 2014, p. 111.
[2] E. National Academies of Sciences, and Medicine, N. A. Press, Ed. Improving Diagnosis in Health Care. 2016.
[3] H. S. o. P. Health, "The Public’s Views On Medical Error In Massachusetts," Betsy Lehman Center for Patient Safety and Medical Error Reduction Health Policy Commission2014.
[4] "Complaints about Acute Trusts 2013-14 and Q1, Q2 2014-15," Parliamentary and Health Service Ombudsman.
[5] "Diagnostic Errors: Technical Series on Safer Primary Care," Geneva: World Health Organization 2016.
[6] R. M. Centor and R. Kumar, "Diagnostic error in community-acquired pneumonia," Diagnosis, vol. 1, no. 2, pp. 145-145, 2014.
[7] Z. Kubilay, A. J. Layon, H. Baer, and L. K. Archibald, "When is pneumonia not pneumonia: a clinicopathologic study of the utility of lung tissue biopsies in determining the suitability of cadaveric tissue for donation," Cell and tissue banking, vol. 17, no. 2, pp. 205-210, 2016.
[8] M. Robert McNutt, M. Richard Abrams, and M. Scott Hasler. (2005). Diagnosing Diagnostic Mistakes. Available:
[9] G. D. Schiff et al., "Diagnostic error in medicine: analysis of 583 physician-reported errors," Archives of internal medicine, vol. 169, no. 20, pp. 1881-1887, 2009.
[10] D. E. Newman-Toker and P. J. Pronovost, "Diagnostic errors—the next frontier for patient safety," Jama, vol. 301, no. 10, pp. 1060-1062, 2009.
[11] H. Singh et al., "Exploring situational awareness in diagnostic errors in primary care," BMJ Qual Saf, vol. 21, no. 1, pp. 30-38, 2012.
[12] L. Medford-Davis, E. Park, G. Shlamovitz, J. Suliburk, A. N. Meyer, and H. Singh, "Diagnostic errors related to acute abdominal pain in the emergency department," Emerg Med J, vol. 33, no. 4, pp. 253-259, 2016.
[13] H. Huang, PACS and imaging informatics: basic principles and applications. John Wiley & Sons, 2011.
[14] R. Nelson and N. Staggers, Health Informatics-E-Book: An Interprofessional Approach. Elsevier Health Sciences, 2016.p.170.
[15] R. Gonzales et al., "A cluster randomized trial of decision support strategies for reducing antibiotic use in acute bronchitis," JAMA internal medicine, vol. 173, no. 4, pp. 267-273, 2013.
[16] R. S. Evans, S. Pestotnik, D. Classen, and J. Burke, "Development of an automated antibiotic consultant," MD computing: computers in medical practice, vol. 10, no. 1, pp. 17-22, 1993.
[17] P. J. Haug et al., "An ontology-driven, diagnostic modeling system," Journal of the American Medical Informatics Association, vol. 20, no. e1, pp. e102-e110, 2013.
[18] O. Bodenreider, "Biomedical ontologies in action: role in knowledge management, data integration and decision support," Yearbook of medical informatics, p. 67, 2008.
[19] V. Kashyap, A. Morales, and T. Hongsermeier, "On implementing clinical decision support: achieving scalability and maintainability by combining business rules and ontologies," in AMIA Annual Symposium Proceedings, 2006, vol. 2006, p. 414: American Medical Informatics Association.
[20] P. Cimiano and J. Völker, "A framework for ontology learning and data-driven change discovery," in Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems (NLDB), Lecture Notes in Computer Science, Springer, 2005, vol. 3513, pp. 227-238: Springer.
[21] A. R. Aronson, "Metamap: Mapping text to the umls metathesaurus," Bethesda, MD: NLM, NIH, DHHS, pp. 1-26, 2006.
[22] P. Grenon, B. Smith, and L. Goldberg, "Biodynamic ontology: applying BFO in the biomedical domain," Studies in health technology and informatics, pp. 20-38, 2004.
[23] J. J. Cimino, "Desiderata for controlled medical vocabularies in the twenty-first century," Methods of information in medicine, vol. 37, no. 4-5, p. 394, 1998.
[24] J. Brank, M. Grobelnik, and D. Mladenić, "A survey of ontology evaluation techniques," 2005.
[25] M. Saeed et al., "Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): a public-access intensive care unit database," Critical care medicine, vol. 39, no. 5, p. 952, 2011.
[26] (2018). Pneumonia Ontology. Available: