{Identifying Personalised Treatments and Clinical Trials for Precision Medicine using Semantic Search with Thalia}
| Type of publication: | Inproceedings |
| Citation: | Przybya2017 |
| Booktitle: | Proceedings of the Twenty-Fifth Text REtrieval Conference (TREC 2017) |
| Year: | 2017 |
| Publisher: | National Insitute of Standards and Technology |
| Address: | Gaithersburg, Maryland |
| URL: | http://trec.nist.gov/pubs/trec... |
| Abstract: | This paper reports the main methods applied in our submission to TREC 2017 Precision Medicine Track. The goal of this challenge was to retrieve documents containing potential treatments and clinical trials for specific patient characteristics. Our main strategy involved using a semantic search engine called Thalia (Text mining for Highlighting, Aggregating and Linking Information in Articles), which allows the recognition of diseases and genes mentioned in text. The recognition of named entities and its linking to concepts in ontologies facilitates more accurate retrieval than just relying on plain textual search and matching. We also highlight the different strategies applied when querying Thalia in the context of this Precision Medicine challenge, which aimed to support different use cases (i.e. more focused or broader searches). |
| Userfields: | file={:home/piotr/Dropbox/UoM/TREC/paper-published/NaCTeM-PM.pdf:pdf}, |
| Keywords: | |
| Authors | |
| Added by: | [PRT] |
| Total mark: | 0 |
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