Extracting Adverse Drug Events from Text Using Human Advice

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.

• Type: Journal
• Year: 2015
• Where: AIME
• Authors: Phillip Odom, Vishal Bangera, Tushar Khot, David Page, Sriraam Natarajan
• Keywords: AIME 2015, Artificial Intelligence