Abstract
Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols. © 2011 ACM.
Original language | English |
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Title of host publication | International Conference on Information and Knowledge Management, Proceedings|Int Conf Inf Knowledge Manage |
Pages | 43-50 |
Number of pages | 7 |
DOIs | |
Publication status | Published - 2011 |
Event | ACM 5th International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO'11, in Conjunction with the 20th ACM International Conference on Information and Knowledge Management, CIKM'11 - Glasgow Duration: 1 Jul 2011 → … |
Conference
Conference | ACM 5th International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO'11, in Conjunction with the 20th ACM International Conference on Information and Knowledge Management, CIKM'11 |
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City | Glasgow |
Period | 1/07/11 → … |
Keywords
- clinical trials
- cluster labels
- clustering
- eligibility criteria
- facet
- term extraction
- terms