Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study.

ABSTRACT:

Background

Accurate detection of bleeding events from electronic health records (EHRs) is crucial for identifying and characterizing different common and serious medical problems. To extract such information from EHRs, it is essential to identify the relations between bleeding events and related clinical entities (eg, bleeding anatomic sites and lab tests). With the advent of natural language processing (NLP) and deep learning (DL)-based techniques, many studies have focused on their applicability for various clinical applications. However, no prior work has utilized DL to extract relations between bleeding events and relevant entities.

Objective

In this study, we aimed to evaluate multiple DL systems on a novel EHR data set for bleeding event-related relation classification

SUBMITTER: Mitra A 

PROVIDER: S-EPMC8285744 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature