An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text

Adverse reaction caused by drugs is a potentially dangerous problem which may lead to mortality and morbidity in patients. Adverse Drug Event (ADE) extraction is a significant problem in biomedical research. We model ADE extraction as a Question-Answering problem and take inspiration from Machine Reading Comprehension (MRC) literature, to design our model.

Our objective in designing such a model is to exploit the local linguistic context in clinical text and enable intra-sequence interaction, in order to jointly learn to classify drug and disease entities and to extract adverse reactions caused by a given drug. Our model makes use of a self-attention mechanism to facilitate intra-sequence interaction in a text sequence. This enables us to visualize and understand how the network makes use of the local and wider context for classification.

Read the full paper for free at the Cornell University Library.

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About Malaikannan Sankarasubbu

mmMalaikannan Sankarasubbu loves anything data and even more if it is un-structured. He has a keen interest in developing high performance Artificial Intelligence based GPU driven solutions for critical problems. With multiple top 10% finishes, he ranks in the top 1000 among 500,000 of the competitive community at Kaggle. He was earlier Founder and CTO of Datalog.ai a valley startup that focussed on Natural Language Understanding and Chatbots.


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