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Selected Publication:

SHR Neuro Cancer Cardio Lipid

Miñarro-Giménez, JA; Kreuzthaler, M; Bernhardt-Melischnig, J; Martínez-Costa, C; Schulz, S.
Acquiring Plausible Predications from MEDLINE by Clustering MeSH Annotations.
Stud Health Technol Inform. 2015; 216(12):716-720


Authors Med Uni Graz:
Bernhardt-Melischnig Johannes
Kreuzthaler Markus Eduard
Martinez Costa Catalina
Minarro-Gimenez Jose Antonio
Schulz Stefan

Dimensions Citations:

Plum Analytics:
The massive accumulation of biomedical knowledge is reflected by the growth of the literature database MEDLINE with over 23 million bibliographic records. All records are manually indexed by MeSH descriptors, many of them refined by MeSH subheadings. We use subheading information to cluster types of MeSH descriptor co-occurrences in MEDLINE by processing co-occurrence information provided by the UMLS. The goal is to infer plausible predicates to each resulting cluster. In an initial experiment this was done by grouping disease-pharmacologic substance co-occurrences into six clusters. Then, a domain expert manually performed the assignment of meaningful predicates to the clusters. The mean accuracy of the best ten generated biomedical facts of each cluster was 85%. This result supports the evidence of the potential of MeSH subheadings for extracting plausible medical predications from MEDLINE.
Find related publications in this database (using NLM MeSH Indexing)
Cluster Analysis -
Data Mining - methods
Knowledge Bases -
MEDLINE - statistics & numerical data
Machine Learning -
Medical Subject Headings -
Natural Language Processing -
Periodicals as Topic - statistics & numerical data
Terminology as Topic -

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