@inproceedings{7dc5905e44694873b17c4f3db047c04b,
title = "Predicting MeSH beyond MEDLINE",
abstract = "Medical subject headings (MeSH) are a flexible and useful tool for describing biomedical concepts. Here, we present MeSHier, a tool for assigning MeSH terms to biomedical documents based on abstract similarity and references to MEDLINE records. When applied to PubMedCentral papers, NIH grants, and USPTO patents we find that these two sources of information produce largely disjoint sets of related MEDLINE records, albeit with some overlap in MeSH. When combined they provide an enriched topical annotation that would not have been possible with either alone. MeSHier is available as a demo tool that can take as input IDs of PubMed papers, USPTO patents, and NIH grants: http://abel.lis.illinois.edu/cgi-bin/meshier/search.py.",
keywords = "Controlled vocabularies, Medical subject headings",
author = "Kehoe, {Adam K.} and Torvik, {Vetle I.} and Smalheiser, {Neil R.} and Ross, {Matthew B.}",
note = "Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; 1st Workshop on Scholarly Web Mining, SWM 2017 ; Conference date: 10-02-2017",
year = "2017",
month = feb,
day = "10",
doi = "10.1145/3057148.3057155",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "49--56",
booktitle = "Proceedings of the 1st Workshop on Scholarly Web Mining, SWM 2017",
address = "United States",
}