Abstract
Chinese Patent Medicines (CPMs) are welcomed by many people around the world, but the lack of information about their Adverse Drug Reactions (ADRs) is a big issue for drug safety. To get this information, we need to analyze from a number of real-world Adverse Drug Events (ADEs). However, current surveillance systems can only capture a small portion of them and there is a significant time lag in processing the reported data. With the rapid growth of E-commerce in recent years, quantities of patient-oriented user comments are posted on social media in real-time, making it of great value to automatically discover ADEs. To this end, we build a dataset containing 17K patient-oriented user posts about CPMs and further propose a new model that jointly performs ADE detection, extraction and normalization. Different from most previous works dealing with these tasks independently, we show how multi-task learning helps tasks to facilitate each other. To better deal with colloquial expressions and confusing statements in user comments, we leverage standard ADR-terms as prior knowledge as well as finding clues from other related comments. Experimental results show that our model outperforms previous work by a substantial margin.
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Chai, Z., Wan, X. (2021). Adverse Drug Events Detection, Extraction and Normalization from Online Comments of Chinese Patent Medicines. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_21
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DOI: https://doi.org/10.1007/978-3-030-75762-5_21
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