Abstract
In the creation of diagnostic decision support systems (DDSS) it is crucial to have validated and precise knowledge in order to create accurate systems. Typically, medical experts are the source of this knowledge, but it is not always possible to obtain all the desired information from them. Another valuable source could be medical books or articles describing the diagnosis of diseases managed by the DDSS, but again, it is not easy to extract this information. In this paper we present the results of our research, in which we have used Web scraping and a combination of natural language processing techniques to extract diagnostic criteria from MedlinePlus articles about infectious diseases.
The erratum of this chapter can be found under DOI 10.1007/978-3-319-19776-0_16
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-19776-0_16
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Acknowledgments
Alejandro Rodríguez González’s and Mark Wilkinson’s work is supported by Isaac Peral Programme of the UPM. Marcos Martínez-Romero work has been supported by a Postdoc Fellowship from the Xunta de Galicia, Spain (ref. POS-A/2013/197).
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Rodríguez-González, A., Martínez-Romero, M., Costumero, R., Wilkinson, M.D., Menasalvas-Ruiz, E. (2015). Diagnostic Knowledge Extraction from MedlinePlus: An Application for Infectious Diseases. In: Overbeek, R., Rocha, M., Fdez-Riverola, F., De Paz, J. (eds) 9th International Conference on Practical Applications of Computational Biology and Bioinformatics. Advances in Intelligent Systems and Computing, vol 375. Springer, Cham. https://doi.org/10.1007/978-3-319-19776-0_9
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