title: Emerging trend prediction in biomedical literature.
abstract: We present a study on how to predict new emerging trends in the biomedical domain based on textual data. We thereby propose a way of anticipating the transformation of arbitrary information into ground truth knowledge by predicting the inclusion of new terms into the MeSH ontology. We also discuss the preparation of a dataset for the evaluation of emerging trend prediction algorithms that is based on PubMed abstracts and related MeSH terms. The results suggest that early prediction of emerging trends is possible.
authors.affiliation: Integrated Data Systems, Siemens Corporate Research, Princeton, NJ, USA.
authors.forename: Fabian
authors.initials: F
authors.lastname: Moerchen
authors.forename: Dmitriy
authors.initials: D
authors.lastname: Fradkin
authors.forename: Mathaeus
authors.initials: M
authors.lastname: Dejori
authors.forename: Bernd
authors.initials: B
authors.lastname: Wachmann
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D016247:Information Storage and Retrieval / Q000379:methods*; D055811:Journal Impact Factor; D046650:Medical Subject Headings / Q000706:statistics & numerical data*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic / Q000145:classification* / Q000639:trends*; D009626:Terminology as Topic; D014481:United States