Representing and extracting lung cancer study metadata: study objective and study design.
Comput Biol Med
; 58: 63-72, 2015 Mar.
Article
en En
| MEDLINE
| ID: mdl-25618216
ABSTRACT
This paper describes the information retrieval step in Casama (Contextualized Semantic Maps), a project that summarizes and contextualizes current research papers on driver mutations in non-small cell lung cancer. Casama׳s representation of lung cancer studies aims to capture elements that will assist an end-user in retrieving studies and, importantly, judging their strength. This paper focuses on two types of study metadata study objective and study design. 430 abstracts on EGFR and ALK mutations in lung cancer were annotated manually. Casama׳s support vector machine (SVM) automatically classified the abstracts by study objective with as much as 129% higher F-scores compared to PubMed׳s built-in filters. A second SVM classified the abstracts by epidemiological study design, suggesting strength of evidence at a more granular level than in previous work. The classification results and the top features determined by the classifiers suggest that this scheme would be generalizable to other mutations in lung cancer, as well as studies on driver mutations in other cancer domains.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
/
Bases de Datos Factuales
/
Biología Computacional
/
Neoplasias Pulmonares
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Systematic_reviews
Límite:
Humans
Idioma:
En
Revista:
Comput Biol Med
Año:
2015
Tipo del documento:
Article