A novel feature-based approach to extract drug-drug interactions from biomedical text.
Bioinformatics
; 30(23): 3365-71, 2014 Dec 01.
Article
em En
| MEDLINE
| ID: mdl-25143286
ABSTRACT
MOTIVATION Knowledge of drug-drug interactions (DDIs) is crucial for health-care professionals to avoid adverse effects when co-administering drugs to patients. As most newly discovered DDIs are made available through scientific publications, automatic DDI extraction is highly relevant. RESULTS:
We propose a novel feature-based approach to extract DDIs from text. Our approach consists of three steps. First, we apply text preprocessing to convert input sentences from a given dataset into structured representations. Second, we map each candidate DDI pair from that dataset into a suitable syntactic structure. Based on that, a novel set of features is used to generate feature vectors for these candidate DDI pairs. Third, the obtained feature vectors are used to train a support vector machine (SVM) classifier. When evaluated on two DDI extraction challenge test datasets from 2011 and 2013, our system achieves F-scores of 71.1% and 83.5%, respectively, outperforming any state-of-the-art DDI extraction system. AVAILABILITY AND IMPLEMENTATION The source code is available for academic use at http//www.biosemantics.org/uploads/DDI.zip.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Interações Medicamentosas
/
Mineração de Dados
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2014
Tipo de documento:
Article