Abstract
Adverse Drug Events (ADE) are negative medical conditions occurring when a drug is used. Social media contains millions of unsolicited and uncensored discussions about medication. Utilizing the health-related social media data for mining drug usage patterns and ADE is the main objective of work reported in this paper. The reviews given by users in social health blogs and forums are collected, preprocessed, and detected for drug names and attributes using named entity recognition methods. Then adverse events are extracted using medical lexicons. Drugs and associated ADEs are extracted using Apriori technique and classified using random forest classifier. The overall ADE detection and extraction are evaluated for its accuracy using traditional IR metrics.
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Acknowledgements
This Publication is an outcome of the R&D work undertaken in the project under the Visvesvaraya PhD Scheme (Unique Awardee Number: VISPHD-MEITY-2959) of Ministry of Electronics and Information Technology, Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia).
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Balaji, A., Sendhilkumar, S., Mahalakshmi, G.S. (2021). Identification of Adverse Drug Events from Social Networks. In: Singh Pundir, A.K., Yadav, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0167-5_10
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DOI: https://doi.org/10.1007/978-981-16-0167-5_10
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