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ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials

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 com...

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Detalles Bibliográficos
Autores principales: Korkontzelos, Ioannis, Mu, Tingting, Ananiadou, Sophia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3339391/
https://www.ncbi.nlm.nih.gov/pubmed/22595088
http://dx.doi.org/10.1186/1472-6947-12-S1-S3
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author Korkontzelos, Ioannis
Mu, Tingting
Ananiadou, Sophia
author_facet Korkontzelos, Ioannis
Mu, Tingting
Ananiadou, Sophia
author_sort Korkontzelos, Ioannis
collection PubMed
description 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.
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spelling pubmed-33393912012-05-02 ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials Korkontzelos, Ioannis Mu, Tingting Ananiadou, Sophia BMC Med Inform Decis Mak Proceedings 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. BioMed Central 2012-04-30 /pmc/articles/PMC3339391/ /pubmed/22595088 http://dx.doi.org/10.1186/1472-6947-12-S1-S3 Text en Copyright ©2012 Korkontzelos et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Korkontzelos, Ioannis
Mu, Tingting
Ananiadou, Sophia
ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials
title ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials
title_full ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials
title_fullStr ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials
title_full_unstemmed ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials
title_short ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials
title_sort ascot: a text mining-based web-service for efficient search and assisted creation of clinical trials
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3339391/
https://www.ncbi.nlm.nih.gov/pubmed/22595088
http://dx.doi.org/10.1186/1472-6947-12-S1-S3
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