Extracting Adverse Drug Events from Text Using Human Advice [chapter]

Phillip Odom, Vishal Bangera, Tushar Khot, David Page, Sriraam Natarajan
2015 Lecture Notes in Computer Science  
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
more » ... 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.
doi:10.1007/978-3-319-19551-3_26 pmid:29119145 pmcid:PMC5673136 fatcat:ytnytfahafhybmbx6g2igi5mja