Abstract
The overwhelmed amount of medical information available in the research literature, makes the use of automated information classification methods essential for both medical experts and novice users. This paper presents a method for classifying medical documents into documents for medical professionals (experts) and non-professionals (consumers), by representing them as term vectors and applying Multiple Criteria Decision Analysis (MCDA) tools to leverage this information. The results show that when medical documents are represented by terms extracted from AMTEx, a medical document indexing method, specifically designed for the automatic indexing of documents in large medical collections, such as MEDLINE, better classification performance is achieved, compared to MetaMap Transfer, the automatic mapping of biomedical documents to UMLS term concepts developed by U.S. National Library of Medicine, or the MeSH method, under which documents are indexed by human experts.
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Lakiotaki, K., Hliaoutakis, A., Koutsos, S., Petrakis, E.G.M. (2013). Towards Personalized Medical Document Classification by Leveraging UMLS Semantic Network. In: Huang, G., Liu, X., He, J., Klawonn, F., Yao, G. (eds) Health Information Science. HIS 2013. Lecture Notes in Computer Science, vol 7798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37899-7_8
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DOI: https://doi.org/10.1007/978-3-642-37899-7_8
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