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Advisor(s)
Abstract(s)
It has been proved that pharmacovigilance benefits from the analysis and extraction of user-generated data from blogs, medical forums or other social networks, regarding adverse effect mentions or complaints that occur from taking certain drugs. Data mining, machine learning, pattern recognition, content summarization, and natural language processing techniques are often used in this field with promising results. However, there are still several difficulties concerning the extraction, as the highly domain-specific vocabulary presents a few challenges. This is mainly because patients like to use idiomatic or vernacular expressions along with descriptive symptom explanations, which tend to deviate from grammatical rules or expected terms. To address this issue, we propose a well-curated baseline. We believe that building a specific lexicon, identifying common linguistic patterns and observing certain phrasal structures is key to first understanding how a user generates contents online. From there, we can later develop sets of tailored rules that will allow data classification/extraction systems to potentially improve their efficiency at these tasks.
Description
Keywords
Pharmacovigilance Adverse effects Information extraction Natural language processing
Citation
D. Abrantes and J. Cordeiro, "Extracting Adverse Drug Effects from User Experiences: A Baseline," 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, 2018, pp. 405-410.
Publisher
Institute of Electrical and Electronics Engineers