Developing Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP.
Stud Health Technol Inform
; 264: 1041-1045, 2019 Aug 21.
Article
em En
| MEDLINE
| ID: mdl-31438083
ABSTRACT
Natural language processing (NLP) technologies have been successfully applied to cancer research by enabling automated phenotypic information extraction from narratives in electronic health records (EHRs) such as pathology reports; however, developing customized NLP solutions requires substantial effort. To facilitate the adoption of NLP in cancer research, we have developed a set of customizable modules for extracting comprehensive types of cancer-related information in pathology reports (e.g., tumor size, tumor stage, and biomarkers), by leveraging the existing CLAMP system, which provides user-friendly interfaces for building customized NLP solutions for individual needs. Evaluation using annotated data at Vanderbilt University Medical Center showed that CLAMP-Cancer could extract diverse types of cancer information with good F-measures (0.80-0.98). We then applied CLAMP-Cancer to an information extraction task at Mayo Clinic and showed that we can quickly build a customized NLP system with comparable performance with an existing system at Mayo Clinic. CLAMP-Cancer is freely available for academic use.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Armazenamento e Recuperação da Informação
/
Neoplasias
Limite:
Humans
Idioma:
En
Revista:
Stud Health Technol Inform
Assunto da revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Ano de publicação:
2019
Tipo de documento:
Article