Developing timely insights into comparative effectiveness research with a text-mining pipeline.
Drug Discov Today
; 21(3): 473-80, 2016 Mar.
Article
em En
| MEDLINE
| ID: mdl-26854423
ABSTRACT
Comparative effectiveness research (CER) provides evidence for the relative effectiveness and risks of different treatment options and informs decisions made by healthcare providers, payers, and pharmaceutical companies. CER data come from retrospective analyses as well as prospective clinical trials. Here, we describe the development of a text-mining pipeline based on natural language processing (NLP) that extracts key information from three different trial data sources NIH ClinicalTrials.gov, WHO International Clinical Trials Registry Platform (ICTRP), and Citeline Trialtrove. The pipeline leverages tailored terminologies to produce an integrated and structured output, capturing any trials in which pharmaceutical products of interest are compared with another therapy. The timely information alerts generated by this system provide the earliest and most complete picture of emerging clinical research.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Mineração de Dados
/
Pesquisa Comparativa da Efetividade
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Drug Discov Today
Assunto da revista:
FARMACOLOGIA
/
TERAPIA POR MEDICAMENTOS
Ano de publicação:
2016
Tipo de documento:
Article