Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3233/978-1-61499-564-7-716 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Knowledge Acquisition,MEDLINE,Biomedical Terminologies | Data mining,Information retrieval,Subject-matter expert,MeSH Descriptors,Types of mesh,Cluster analysis,Unified Medical Language System,MEDLINE,Medicine | Conference |
Volume | ISSN | Citations |
216 | 0926-9630 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Antonio Miñarro-Giménez | 1 | 60 | 12.59 |
| Markus Kreuzthaler | 2 | 76 | 11.15 |
| Johannes Bernhardt-Melischnig | 3 | 5 | 1.12 |
| Catalina Martínez-Costa | 4 | 163 | 17.99 |
| Stefan Schulz | 5 | 1092 | 127.03 |