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Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text

Drug toxicity is a major concern for both regulatory agencies and the pharmaceutical industry. In this context, text-mining methods for the identification of drug side effects from free text are key for the development of up-to-date knowledge sources on drug adverse reactions. We present a new syste...

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Autores principales: Bravo, Àlex, Li, Tong Shu, Su, Andrew I., Good, Benjamin M., Furlong, Laura I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908671/
https://www.ncbi.nlm.nih.gov/pubmed/27307137
http://dx.doi.org/10.1093/database/baw094
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author Bravo, Àlex
Li, Tong Shu
Su, Andrew I.
Good, Benjamin M.
Furlong, Laura I.
author_facet Bravo, Àlex
Li, Tong Shu
Su, Andrew I.
Good, Benjamin M.
Furlong, Laura I.
author_sort Bravo, Àlex
collection PubMed
description Drug toxicity is a major concern for both regulatory agencies and the pharmaceutical industry. In this context, text-mining methods for the identification of drug side effects from free text are key for the development of up-to-date knowledge sources on drug adverse reactions. We present a new system for identification of drug side effects from the literature that combines three approaches: machine learning, rule- and knowledge-based approaches. This system has been developed to address the Task 3.B of Biocreative V challenge (BC5) dealing with Chemical-induced Disease (CID) relations. The first two approaches focus on identifying relations at the sentence-level, while the knowledge-based approach is applied both at sentence and abstract levels. The machine learning method is based on the BeFree system using two corpora as training data: the annotated data provided by the CID task organizers and a new CID corpus developed by crowdsourcing. Different combinations of results from the three strategies were selected for each run of the challenge. In the final evaluation setting, the system achieved the highest Recall of the challenge (63%). By performing an error analysis, we identified the main causes of misclassifications and areas for improving of our system, and highlighted the need of consistent gold standard data sets for advancing the state of the art in text mining of drug side effects. Database URL: https://zenodo.org/record/29887?ln¼en#.VsL3yDLWR_V
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spelling pubmed-49086712016-06-17 Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text Bravo, Àlex Li, Tong Shu Su, Andrew I. Good, Benjamin M. Furlong, Laura I. Database (Oxford) Database Tool Drug toxicity is a major concern for both regulatory agencies and the pharmaceutical industry. In this context, text-mining methods for the identification of drug side effects from free text are key for the development of up-to-date knowledge sources on drug adverse reactions. We present a new system for identification of drug side effects from the literature that combines three approaches: machine learning, rule- and knowledge-based approaches. This system has been developed to address the Task 3.B of Biocreative V challenge (BC5) dealing with Chemical-induced Disease (CID) relations. The first two approaches focus on identifying relations at the sentence-level, while the knowledge-based approach is applied both at sentence and abstract levels. The machine learning method is based on the BeFree system using two corpora as training data: the annotated data provided by the CID task organizers and a new CID corpus developed by crowdsourcing. Different combinations of results from the three strategies were selected for each run of the challenge. In the final evaluation setting, the system achieved the highest Recall of the challenge (63%). By performing an error analysis, we identified the main causes of misclassifications and areas for improving of our system, and highlighted the need of consistent gold standard data sets for advancing the state of the art in text mining of drug side effects. Database URL: https://zenodo.org/record/29887?ln¼en#.VsL3yDLWR_V Oxford University Press 2016-06-15 /pmc/articles/PMC4908671/ /pubmed/27307137 http://dx.doi.org/10.1093/database/baw094 Text en © The Author(s) 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Database Tool
Bravo, Àlex
Li, Tong Shu
Su, Andrew I.
Good, Benjamin M.
Furlong, Laura I.
Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text
title Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text
title_full Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text
title_fullStr Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text
title_full_unstemmed Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text
title_short Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text
title_sort combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text
topic Database Tool
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908671/
https://www.ncbi.nlm.nih.gov/pubmed/27307137
http://dx.doi.org/10.1093/database/baw094
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