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.
CITATION STYLE
Odom, P., Bangera, V., Khot, T., Page, D., & Natarajan, S. (2015). Extracting adverse drug events from text using human advice. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9105, pp. 195–204). Springer Verlag. https://doi.org/10.1007/978-3-319-19551-3_26
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