Extracting Adverse Drug Events from Text using Human Advice.
Artif Intell Med Conf Artif Intell Med (2005-)
; 2015: 195-204, 2015.
Article
en En
| MEDLINE
| ID: mdl-29119145
Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society in general. When methods extract ADEs from observational data, there is a necessity to evaluate these methods. More precisely, it is important to know what is already known in the literature. Consequently, we employ a novel relation extraction technique based on a recently developed probabilistic logic learning algorithm that exploits human advice. We demonstrate on a standard adverse drug events data base that the proposed approach can successfully extract existing adverse drug events from limited amount of training data and compares favorably with state-of-the-art probabilistic logic learning methods.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Artif Intell Med Conf Artif Intell Med (2005-)
Año:
2015
Tipo del documento:
Article
Pais de publicación:
Alemania