NDLI: Anticipating annotations and emerging trends in biomedical literature

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Anticipating annotations and emerging trends in biomedical literature

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Anticipating annotations and emerging trends in biomedical literature

Content Provider	ACM Digital Library
Author	Fradkin, Dmitriy Etienne, Julien Mörchen, Fabian Bundschus, Markus Wachmann, Bernd Dejori, Mathäus
Abstract	The BioJournalMonitor is a decision support system for the analysis of trends and topics in the biomedical literature. Its main goal is to identify potential diagnostic and therapeutic biomarkers for specific diseases. Several data sources are continuously integrated to provide the user with up-to-date information on current research in this field. State-of-the-art text mining technologies are deployed to provide added value on top of the original content, including named entity detection, relation extraction, classification, clustering, ranking, summarization, and visualization. We present two novel technologies that are related to the analysis of temporal dynamics of text archives and associated ontologies. Currently, the MeSH ontology is used to annotate the scientific articles entering the PubMed database with medical terms. Both the maintenance of the ontology as well as the annotation of new articles is performed largely manually. We describe how probabilistic topic models can be used to annotate recent articles with the most likely MeSH terms. This provides our users with a competitive advantage because, when searching for MeSH terms, articles are found long before they are manually annotated. We further present a study on how to predict the inclusion of new terms in the MeSH ontology. The results suggest that early prediction of emerging trends is possible. The trend ranking functions are deployed in our system to enable interactive searches for the hottest new trends relating to a disease.
Starting Page	954
Ending Page	962
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1402004
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text mining Prediction pubmed Lda Mesh Trends
Content Type	Text
Resource Type	Article

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