Abstract
The mining of adverse drug reaction (ADR)
has a crucial role in the pharmacovigilance.
The traditional ways of identifying ADR are
reliable but time-consuming, non-scalable and
offer a very limited amount of ADR relevant
information. With the unprecedented growth
of information sources in the forms of social media texts (Twitter, Blogs, Reviews etc.),
biomedical literature, and Electronic Medical
Records (EMR), it has become crucial to extract the most pertinent ADR related information from these free-form texts. In this paper,
we propose a neural network inspired multitask learning framework that can simultaneously extract ADRs from various sources. We
adopt a novel adversarial learning-based approach to learn features across multiple ADR
information sources. Unlike the other existing techniques, our approach is capable to extracting fine-grained information (such as ‘Indications’, ‘Symptoms’, ‘Finding’, ‘Disease’,
‘Drug’) which provide important cues in pharmacovigilance. We evaluate our proposed
approach on three publicly available realworld benchmark pharmacovigilance datasets,
a Twitter dataset from PSB 2016 Social Media Shared Task, CADEC corpus and Medline
ADR corpus. Experiments show that our uni-
fied framework achieves state-of-the-art performance on individual tasks associated with
the different benchmark datasets. This establishes the fact that our proposed approach is
generic, which enables it to achieve high performance on the diverse datasets. The source
code is available here