JMIR Publications

ABSTRACT

Background:

As drug prescriptions are often recorded in free-text clinical narratives, extracting such information is important to support complex health-related tasks. Several natural language processing (NLP) methods have been proposed to extract such information, but still with limited performance.

Objective:

This paper describes (DrugEx), which extracts drugs and their attributes from clinical free-text notes. The study aims to evaluate the feasibility of using NLP and deep learning approaches for extracting and linking drug-associated attributes. It also presents an extensive error analysis of different methods. This effort was part of the 2018 National NLP Clinical Challenges (n2c2) Shared Task on Adverse Drug Events and Medication Extraction.

Methods:

The proposed method (DrugEx) consists of a named entity recogniser (NER) to identify drugs and associated attributes, and relation extraction (RE) component to identify relations between them. For the NER, we explored deep learning-based approaches (i.e. Bi-LSTM-CRFs) with various embeddings (i.e. word, character and semantic-feature embeddings) in order to investigate how different embeddings influence the performance. For RE, a rule-based method was implemented and compared with a positional-aware LSTM model. The methods were trained and evaluated using the 2018 n2c2 shared-task data.

Results:

Experiments showed that the best model (Bi-LSTM-CRFs with words and character embeddings) achieved lenient micro F-scores of 0.921 for NER, 0.927 for RE and 0.855 for the end-to-end system.

Conclusions:

The proposed end-to-end system achieves encouraging results and demonstrates the feasibility of using deep learning methods for extracting medication information from free-text data.