Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
This addresses drug safety monitoring by improving automated extraction from medical texts, but it is incremental as it builds on existing sequence labeling methods.
The paper tackled the problem of extracting adverse drug reactions and related entities from text, achieving F1-scores of 76.00 and 75.61 in a 2017 shared task.
Adverse drug reactions (ADRs) are unwanted or harmful effects experienced after the administration of a certain drug or a combination of drugs, presenting a challenge for drug development and drug administration. In this paper, we present a set of taggers for extracting adverse drug reactions and related entities, including factors, severity, negations, drug class and animal. The systems used a mix of rule-based, machine learning (CRF) and deep learning (BLSTM with word2vec embeddings) methodologies in order to annotate the data. The systems were submitted to adverse drug reaction shared task, organised during Text Analytics Conference in 2017 by National Institute for Standards and Technology, archiving F1-scores of 76.00 and 75.61 respectively.