Entity relation extraction from electronic medical records based on improved annotation rules and BiLSTM-CRF.

ABSTRACT:

Background

Extracting entities and their relationships from electronic medical records (EMRs) is an important research direction in the development of medical informatization. Recently, a method was proposed to transform entity relation extraction into entity recognition by using annotation rules, and then solve the problem of relation extraction by an entity recognition model. However, this method cannot deal with one-to-many entity relationship problems.

Methods

This paper combined the bidirectional long- and short-term memory-conditional random field (BiLSTM-CRF) deep learning model with an improvement of sequence annotation rules, hided relationships between entities in entity labels, then the problem of one-to-many named entity relation extraction in EMRs was transforme

SUBMITTER: Chen T 

PROVIDER: S-EPMC8506757 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature