Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting.

ABSTRACT:

Objective

To develop a natural language processing system that identifies relations of medications with adverse drug events from clinical narratives. This project is part of the 2018 n2c2 challenge.

Materials and methods

We developed a novel clinical named entity recognition method based on an recurrent convolutional neural network and compared it to a recurrent neural network implemented using the long-short term memory architecture, explored methods to integrate medical knowledge as embedding layers in neural networks, and investigated 3 machine learning models, including support vector machines, random forests and gradient boosting for relation classification. The performance of our system was evaluated using annotated data and scripts provided by the 2018 n2c2 organizers

SUBMITTER: Yang X 

PROVIDER: S-EPMC7489076 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature