Our results show that our method is an effective way of discovering side effects in tweets with an improvement from 53% F-measure to 67% F-measure as compared ...
To remedy this, semi-supervised active learning may reduce the number of labeled data needed and focus the manual labeling process on important information.
Jan 27, 2020 · Motivation: Social media is a largely untapped source of information on side effects of drugs. Twitter in particular is widely used to report on ...
Motivation: Social media is a largely untapped source of information on side effects of drugs. Twitter in particular is widely used to report on everyday ...
This paper uses Amazon Mechanical Turk in combination with a state-of-the-art semi-supervised active learning method to label tweets with their associated ...
Our results show that our method is an effective way of discovering side effects in tweets with an improvement from 53% F-measure to 67% F-measure as compared ...
Towards Identifying Drug Side Effects from Social Media Using Active Learning andCrowd Sourcing ... with legal nonprofit status through Code for Science ...
Adverse drug reactions (ADRs) are a huge public health issue. Identifying text that mentions ADRs from a large volume of social media data is important.
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Identifying Adverse Drug Reaction-Related Text from Social Media: A Multi-View Active Learning Approach with Various Document Representations. Language ...
May 3, 2023 · Social media data, which includes information on drug usage by users, can be used to detect drug abuse and medication compliance in patients.