Indexing Publicly Available Health Data with Medical Subject Headings (MeSH): An Evaluation of Term Coverage.
As part of the Open Government Initiative, the United States federal government published datasets to increase collaboration, transparency, consumer participation, and research, and are available online at HealthData.gov. Currently, HealthData.gov does not adequately support the accessibility goal of the Open Government Initiative due to issues of retrieving relevant data because of inadequately cataloguing and lack of indexing with a standardized terminology. Given the commonalities between the HealthData.gov and MEDLINE metadata, Medical Subject Headings (MeSH) may offer an indexing solution, but there needs to be a formal evaluation of the efficacy of MeSH for covering the dataset concepts. The purpose of this study was to determine if MeSH adequately covers the HealthData.gov concepts. The noun and noun phrases from the HealthData.gov metadata were extracted and mapped to MeSH using MetaMap. The frequency of no exact, partical and no matches with MeSH terms were determined. The results of this study revealed that about 70% of the HealthData.gov concepts partially or exactly matched MeSH terms. Therefore, MeSH may be a favorable terminology for indexing the HealthData.gov datasets.
Institute for Health Informatics - University of Minnesota- Twin Cities, Minneapolis, MN.
David T
DT
Marc
Institute for Health Informatics - University of Minnesota- Twin Cities, Minneapolis, MN.
Rui
R
Zhang
Bio-Medical Library- University of Minnesota- Twin Cities, Minneapolis, MN.
James
J
Beattie
Institute for Health Informatics - University of Minnesota- Twin Cities, Minneapolis, MN.
Laël C
LC
Gatewood
Institute for Health Informatics - University of Minnesota- Twin Cities, Minneapolis, MN.
Saif S
SS
Khairat
D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods / Q000706:statistics & numerical data; D016239:MEDLINE / Q000706:statistics & numerical data*; D046650:Medical Subject Headings; D009323:Natural Language Processing; D011634:Public Health / Q000706:statistics & numerical data*; D012660:Semantics; D009626:Terminology as Topic; D014481:United States