Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.

ABSTRACT:

Objective

Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records.

Materials and methods

We proposed an ensemble approach for relation extraction and classification between drugs and medication-related entities. We incorporated state-of-the-art named-entity recognition (NER) models based on bidirectional long short-term memory (BiLSTM) networks and conditional random fields (CRF) for end-to-end extraction. We additionally developed separate models for intra- and inter-sentence relation extraction and combined

SUBMITTER: Christopoulou F 

PROVIDER: S-EPMC6913215 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature