An ensemble of neural models for nested adverse drug events and medication extraction with subwords.

ABSTRACT:

Objective

This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2.

Materials and methods

We designed a neural model to tackle both nested (entities embedded in other entities) and polysemous entities (entities annotated with multiple semantic types) based on MIMIC III discharge summaries. To better represent rare and unknown words in entities, we further tokenized the MIMIC III data set by splitting the words into finer-grained subwords. We finally combined all the models to boost the performance. Additionally, we implemented a featured-based conditional random field model and created an ensemble to combine its predictions with those

SUBMITTER: Ju M 

PROVIDER: S-EPMC6913208 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature