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SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis

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Abstract

Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of semantic descriptions. The objective of this paper is to propose a normalized design that solves some of the problems which have been detected by authors in previous tools. The authors bring together two different technologies to develop a new clinical decision support system: description logics aimed at developing inference systems to improve decision support for the prevention, treatment and management of illness and semantic technologies. Because of its new design, the system is capable of obtaining improved diagnostics compared with previous efforts. However, this evaluation is more focused in the computational performance, giving as result that description logics is a good solution with small data sets. In this paper, we provide a well-structured ontology for automated diagnosis in the medical field and a three-fold formalization based on Description Logics with the use of Semantic Web technologies.

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Acknowledgements

This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project TRAZAMED (IPT-090000-2010-007).

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Authors and Affiliations

  1. Computer Science Department, Universidad Carlos III de Madrid, Av. Universidad 30, Leganés, 28911, Madrid, Spain

    Alejandro Rodríguez-González, Ricardo Colomo-Palacios, Juan Miguel Gómez-Berbís & Angel García-Crespo

  2. Computer Science Department, Universidad de Oviedo, Valdes Salas Street S/N, Oviedo, 33007, Spain

    Jose Emilio Labra-Gayo

  3. Research Programme on Biomedical Informatics (GRIB), IMIM-Universitat Pompeu Fabra, Dr. Aiguader, 88, Barcelona, 08003, Spain

    Miguel A. Mayer

Authors
  1. Alejandro Rodríguez-González
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  2. Jose Emilio Labra-Gayo
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Correspondence to Alejandro Rodríguez-González.

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Rodríguez-González, A., Labra-Gayo, J.E., Colomo-Palacios, R. et al. SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis. J Med Syst 36, 2471–2481 (2012). https://doi.org/10.1007/s10916-011-9714-1

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