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Towards personalized medical document classificationby leveraging UMLS Ssemantic network

Petrakis Evripidis, Lakiotaki Kleanthi, Chliaoutakis Angelos, Koutsos Serafeim

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/7B0AE011-CBD1-4018-9CEA-94889C6C7F66-
Αναγνωριστικόhttps://doi.org/10.1007/978-3-642-37899-7_8-
Γλώσσαen-
ΤίτλοςTowards personalized medical document classification by leveraging UMLS Ssemantic networken
ΔημιουργόςPetrakis Evripidisen
ΔημιουργόςΠετρακης Ευριπιδηςel
ΔημιουργόςLakiotaki Kleanthien
ΔημιουργόςΛακιωτακη Κλεανθηel
ΔημιουργόςChliaoutakis Angelosen
ΔημιουργόςΧλιαουτακης Αγγελοςel
ΔημιουργόςKoutsos Serafeimen
ΔημιουργόςΚουτσος Σεραφειμel
ΕκδότηςSpringer Verlagen
ΠερίληψηThe 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.en
ΤύποςΠερίληψη Δημοσίευσης σε Συνέδριοel
ΤύποςConference Paper Abstracten
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-31-
Ημερομηνία Δημοσίευσης2013-
Βιβλιογραφική ΑναφοράKleanthi Lakiotaki, Angelos Hliaoutakis, Serafim Koutsos, Euripides G.M. Petrakis, "Towards Personalized Medical Document Classification by Leveraging UMLS Semantic Network", in 2nd International Conference on Health Information Science (HIS2013), 2013, pp.93-104. doi:10.1007/978-3-642-37899-7_8en

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