Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.7/400
Título: Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature
Autor: Kolchinsky, Artemy
Lourenço, Anália
Wu, Heng-Yi
Li, Lang
Rocha, Luis M.
Palavras-chave: Statistics - Machine Learning
Computer Science - Information Retrieval
Quantitative Biology - Quantitative Methods
Data: 11-Mai-2015
Editora: PLOS
Citação: Kolchinsky A, Lourenço A, Wu H-Y, Li L, Rocha LM (2015) Extraction of Pharmacokinetic Evidence of Drug – Drug Interactions from the Literature. PLoS ONE 10(5): e0122199. doi:10.1371/ journal.pone.0122199
Resumo: Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmaco-epidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1~=0.93, MCC~=0.74, iAUC~=0.99) and sentences (F1~=0.76, MCC~=0.65, iAUC~=0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. ...
Peer review: yes
URI: http://hdl.handle.net/10400.7/400
DOI: 10.1371/journal.pone.0122199
Versão do Editor: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0122199
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