Cargando…
Identifying the status of genetic lesions in cancer clinical trial documents using machine learning
BACKGROUND: Many cancer clinical trials now specify the particular status of a genetic lesion in a patient's tumor in the inclusion or exclusion criteria for trial enrollment. To facilitate search and identification of gene-associated clinical trials by potential participants and clinicians, it...
Autores principales: | Wu, Yonghui, Levy, Mia A, Micheel, Christine M, Yeh, Paul, Tang, Buzhou, Cantrell, Michael J, Cooreman, Stacy M, Xu, Hua |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3535695/ https://www.ncbi.nlm.nih.gov/pubmed/23282337 http://dx.doi.org/10.1186/1471-2164-13-S8-S21 |
Ejemplares similares
-
Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
por: Tang, Buzhou, et al.
Publicado: (2013) -
Document-level medical relation extraction via edge-oriented graph neural network based on document structure and external knowledge
por: Li, Tao, et al.
Publicado: (2021) -
Landscape Analysis of Breast Cancer and Acute Myeloid Leukemia Trials Using the My Cancer Genome Clinical Trial Data Model
por: Jain, Neha M., et al.
Publicado: (2021) -
A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature
por: Tang, Buzhou, et al.
Publicado: (2015) -
Conceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow
por: Jain, Neha M., et al.
Publicado: (2019)