Abstract
In this article, we focus on access to data that can help clinicians in the medical diagnostic process before proposing appropriate treatment. With the explosion of medical knowledge, we are interested to structure them into the informations collection step. We propose an ontology resulting from a fusion of several existing and open medical ontologies and terminologies. On the other hand, we exploit real cases of patients to improve the list of signs of each disease. This work leads to a knowledge base (KB) associating all human diseases with their relevant signs. Cases are also stored in the KB. Each disease is described by all the signs observed and verified in all the patients carrying this same disease. The association of sickness and its signs is thus continuously nourished as there are new cases of diagnosis.
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Example from http://www.immunologyclinic.com/.
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Examples in http://medecinetropicale.free.fr/.
References
Anbarasi, M.S., Naveen, P., Selvaganapathi, S., Mohamed Nowsath Ali, I.: Ontology based medical diagnosis decision support system. Int. J. Eng. Res. Technol. (IJERT) (2013)
Balogh, E.P., Miller, B.T., Ball, J.R.: Improving Diagnosis in Health Care, National Academies of Sciences, Engineering, and Medicine. The National Academies Press, Washington, DC (2015)
Reyes-Ortiz, J.A., Jimenez, A.L., Cater, J., Malendés, C.A.: Ontology-based Knowledge Representation for Supporting Medical Decisions, Recherche in Computer Science (2013)
Mohammed, O., Benlamri, R., Fong, S.: Building a diseases symptoms ontology for medical diagnosis: an integrative approach. In: IEEE International Conference on Future Generation Commnication Technology (FGCT 2012), Décembre 2012
Hoehndorf, R., Schofield, P.N., Gkoutos, G.V.: The role of ontologies in biological and biomedical research: a functional perspective. Brief. Bioinform. J. 16, 1069–1080 (2015)
Oberkampf, H., Zillner, S., Bauer, B.: Interpreting patient data using medical background knowledge. In: Proceedings of the 3rd International Conference on Biomedical Ontology (ICB0), Austria, 21–25 July 2012
Xie, J., Liu, F., Guan, S.-U.: Tree-structure based ontology integration. J. Inf. Sci. 37(6), 594–613 (2011)
Alizadeh, M., Shahrezaei, M.H., Tahernezhad-Javazm, F.: Ontology based information integration: a survey. In: Proceedings of arXiv e-prints (2019)
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Sow, A., Guissé, A., Niang, O. (2020). Data Fusion Model from Coupling Ontologies and Clinical Reports to Guide Medical Diagnosis Process. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1159. Springer, Cham. https://doi.org/10.1007/978-3-030-45688-7_25
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DOI: https://doi.org/10.1007/978-3-030-45688-7_25
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