Clinical Context-Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation.

ABSTRACT:

Background

Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization.

Objective

Traditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical

SUBMITTER: Afzal M 

PROVIDER: S-EPMC7647812 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature