DeepADEMiner is a system for extracting Adverse Drug Effect (ADE) (previously often described as Adverse Drug Reaction (ADR)) spans from user generated texts like tweets from Twitter or posts in Reddit. From a large collection of texts that contain drug/medication mentions, it extracts the spans where the ADEs are mentioned and normalizes them to their respective MedDRA preferred term identifiers. This work uses off-the-shelf named entity recognition (NER) and classification tools based on classical machine learning and newer deep learning frameworks to accomplish the above tasks.
DeepADEMiner is available online as a demo and as a REST API.
Demo website: https://hlp.ibi.upenn.edu/deepademiner/
API documentation: https://bitbucket.org/pennhlp/deepademiner/src/docs/api.md
Code on Bitbucket: https://bitbucket.org/pennhlp/deepademiner/src
[The source code and data for DeepADEMiner will be made available publicly on the date of peer-review article being published.]