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Automated Diagnosis through Ontologies and Logical Descriptions: The ADONIS Approach

Automated Diagnosis through Ontologies and Logical Descriptions: The ADONIS Approach

Alejandro Rodríguez-González, Ángel García-Crespo, Ricardo Colomo-Palacios, José Emilio Labra Gayo, Juan Miguel Gómez-Berbís, Giner Alor-Hernández
Copyright: © 2013 |Pages: 18
ISBN13: 9781466640023|ISBN10: 1466640022|EISBN13: 9781466640030
DOI: 10.4018/978-1-4666-4002-3.ch007
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MLA

Rodríguez-González, Alejandro, et al. "Automated Diagnosis through Ontologies and Logical Descriptions: The ADONIS Approach." Engineering Effective Decision Support Technologies: New Models and Applications, edited by Daniel J. Power, IGI Global, 2013, pp. 117-134. https://doi.org/10.4018/978-1-4666-4002-3.ch007

APA

Rodríguez-González, A., García-Crespo, Á., Colomo-Palacios, R., Gayo, J. E., Gómez-Berbís, J. M., & Alor-Hernández, G. (2013). Automated Diagnosis through Ontologies and Logical Descriptions: The ADONIS Approach. In D. Power (Ed.), Engineering Effective Decision Support Technologies: New Models and Applications (pp. 117-134). IGI Global. https://doi.org/10.4018/978-1-4666-4002-3.ch007

Chicago

Rodríguez-González, Alejandro, et al. "Automated Diagnosis through Ontologies and Logical Descriptions: The ADONIS Approach." In Engineering Effective Decision Support Technologies: New Models and Applications, edited by Daniel J. Power, 117-134. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-4002-3.ch007

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Abstract

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

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