%0 Conference Paper %1 Korkontzelos:2011:TME:2064696.2064706 %A Korkontzelos, Ioannis %A Mu, Tingting %A Restificar, Angelo %A Ananiadou, Sophia %B Proceedings of the ACM fifth international workshop on Data and text mining in biomedical informatics %C New York, NY, USA %D 2011 %I ACM %K CAT CAT-parameter %P 43--50 %R http://doi.acm.org/10.1145/2064696.2064706 %T Text mining for efficient search and assisted creation of clinical trials %U http://doi.acm.org/10.1145/2064696.2064706 %X 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. %@ 978-1-4503-0960-8

@inproceedings{Korkontzelos:2011:TME:2064696.2064706, 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.}, acmid = {2064706}, added-at = {2011-12-12T20:28:58.000+0100}, address = {New York, NY, USA}, author = {Korkontzelos, Ioannis and Mu, Tingting and Restificar, Angelo and Ananiadou, Sophia}, biburl = {https://www.bibsonomy.org/bibtex/2604ef8e25cc350632d6a3bb0b62dca30/huiyangsfsu}, booktitle = {Proceedings of the ACM fifth international workshop on Data and text mining in biomedical informatics}, doi = {http://doi.acm.org/10.1145/2064696.2064706}, interhash = {1bd1da7365704d50349eaa537fd2d4d2}, intrahash = {604ef8e25cc350632d6a3bb0b62dca30}, isbn = {978-1-4503-0960-8}, keywords = {CAT CAT-parameter}, location = {Glasgow, Scotland, UK}, numpages = {8}, pages = {43--50}, publisher = {ACM}, series = {DTMBIO '11}, timestamp = {2011-12-12T20:28:58.000+0100}, title = {Text mining for efficient search and assisted creation of clinical trials}, url = {http://doi.acm.org/10.1145/2064696.2064706}, year = 2011 }