Diagnostic Knowledge Extraction from MedlinePlus: An Application for Infectious Diseases
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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.
KeywordsDiagnostic knowledge Information extraction CDSS DDSS NLP
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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