A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging.

ABSTRACT:

Background

Computed tomography (CT) reports record a large volume of valuable information about patients' conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic study. However, the free-text nature of clinical reports is a critical barrier to use this data more effectively. In this study, we investigate a novel deep learning method to extract entities from Chinese CT reports for lung cancer screening and TNM staging.

Methods

The proposed approach presents a new named entity recognition algorithm, namely the BERT-based-BiLSTM-Transformer network (BERT-BTN) with pre-training, to extract clinical entities for lung cancer screening and staging. Specifically, instead of traditional word embeddi

SUBMITTER: Zhang H 

PROVIDER: S-EPMC8323233 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature