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Monitoring the administration of drugs and adverse drug reactions are keyparts of pharmacovigilance. In this paper, we explore the extraction of drugmentions and drug-related information (reason for taking a drug, route,frequency, dosage, strength, form, duration, and adverse events) from hospitaldischarge summaries through deep learning that relies on variousrepresentations for clinical named entity recognition. This work was officiallypart of the 2018 n2c2 shared task, and we use the data supplied as part of thetask. We developed two deep learning architecture based on recurrent neuralnetworks and pre-trained language models. We also explore the effect ofaugmenting word representations with semantic features for clinical namedentity recognition. Our feature-augmented BiLSTM-CRF model performed withF1-score of 92.67% and ranked 4th for entity extraction sub-task amongsubmitted systems to n2c2 challenge. The recurrent neural networks that use thepre-trained domain-specific word embeddings and a CRF layer for labeloptimization perform drug, adverse event and related entities extraction withmicro-averaged F1-score of over 91%. The augmentation of word vectors withsemantic features extracted using available clinical NLP toolkits can furtherimprove the performance. Word embeddings that are pre-trained on a largeunannotated corpus of relevant documents and further fine-tuned to the taskperform rather well. However, the augmentation of word embeddings with semanticfeatures can help improve the performance (primarily by boosting precision) ofdrug-related named entity recognition from electronic health records.

GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries