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

TL;DR

This paper presents a deep learning approach using feature-augmented BiLSTM-CRF models for extracting drug-related entities from hospital discharge summaries, achieving high accuracy and demonstrating the benefit of semantic feature augmentation.

Contribution

The work introduces a novel feature-augmented BiLSTM-CRF model for clinical named entity recognition, showing that semantic feature augmentation improves performance over standard embeddings.

Findings

Achieved an F1-score of 92.67% in entity extraction.

Semantic feature augmentation boosts precision in drug-related entity recognition.

Pre-trained domain-specific embeddings perform well when fine-tuned for the task.

Abstract

Monitoring the administration of drugs and adverse drug reactions are key parts of pharmacovigilance. In this paper, we explore the extraction of drug mentions and drug-related information (reason for taking a drug, route, frequency, dosage, strength, form, duration, and adverse events) from hospital discharge summaries through deep learning that relies on various representations for clinical named entity recognition. This work was officially part of the 2018 n2c2 shared task, and we use the data supplied as part of the task. We developed two deep learning architecture based on recurrent neural networks and pre-trained language models. We also explore the effect of augmenting word representations with semantic features for clinical named entity recognition. Our feature-augmented BiLSTM-CRF model performed with F1-score of 92.67% and ranked 4th for entity extraction sub-task among submitted systems to n2c2 challenge. The recurrent neural networks that use the pre-trained domain-specific word embeddings and a CRF layer for label optimization perform drug, adverse event and related entities extraction with micro-averaged F1-score of over 91%. The augmentation of word vectors with semantic features extracted using available clinical NLP toolkits can further improve the performance. Word embeddings that are pre-trained on a large unannotated corpus of relevant documents and further fine-tuned to the task perform rather well. However, the augmentation of word embeddings with semantic features can help improve the performance (primarily by boosting precision) of drug-related named entity recognition from electronic health records.