Text Mining Driven Drug-Drug Interaction Detection.

TitleText Mining Driven Drug-Drug Interaction Detection.
Publication TypeJournal Article
Year of Publication2013
AuthorsYan, S, Jiang, X, Chen, Y
JournalProceedings (IEEE Int Conf Bioinformatics Biomed)
Pagination349-355
Date Published2013
ISSN2156-1125
iDASH CategoryNatural Language Processing
Abstract<p>Identifying drug-drug interactions is an important and challenging problem in computational biology and healthcare research. There are accurate, structured but limited domain knowledge and noisy, unstructured but abundant textual information available for building predictive models. The difficulty lies in mining the true patterns embedded in text data and developing efficient and effective ways to combine heterogenous types of information. We demonstrate a novel approach of leveraging augmented text-mining features to build a logistic regression model with improved prediction performance (in terms of discrimination and calibration). Our model based on synthesized features significantly outperforms the model trained with only structured features (AUC: 96% vs. 91%, Sensitivity: 90% vs. 82% and Specificity: 88% vs. 81%). Along with the quantitative results, we also show learned "latent topics", an intermediary result of our text mining module, and discuss their implications.</p>
DOI10.1109/BIBM.2013.6732517
Alternate JournalProceedings (IEEE Int Conf Bioinformatics Biomed)
PubMed ID25131635
PubMed Central IDPMC4133978
Grant ListR00 LM011392 / LM / NLM NIH HHS / United States
U54 HL108460 / HL / NHLBI NIH HHS / United States