Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.

ABSTRACT:

Objective

We sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text.

Materials and methods

Two tasks were performed in this study. For the named entity recognition (NER) task, Bi-LSTM-CRF (bidirectional long short-term memory conditional random field) and BERT (bidirectional encoder representations from transformers) models were trained and compared with CRF model as a baseline to recognize the named entities of DSs and events from clinical notes. In the relation extraction (RE) task, 2 deep learning models, including attention-based Bi-LSTM and convolutional neural network as well as a random forest model were trained to extract the relations between DSs and events, w

SUBMITTER: Fan Y 

PROVIDER: S-EPMC7936508 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature