Extracting entities with attributes in clinical text via joint deep learning.

ABSTRACT:

Objective

Extracting clinical entities and their attributes is a fundamental task of natural language processing (NLP) in the medical domain. This task is typically recognized as 2 sequential subtasks in a pipeline, clinical entity or attribute recognition followed by entity-attribute relation extraction. One problem of pipeline methods is that errors from entity recognition are unavoidably passed to relation extraction. We propose a novel joint deep learning method to recognize clinical entities or attributes and extract entity-attribute relations simultaneously.

Materials and methods

The proposed method integrates 2 state-of-the-art methods for named entity recognition and relation extraction, namely bidirectional long short-term memory with conditional random field and bi

SUBMITTER: Shi X 

PROVIDER: S-EPMC7647140 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature