Large-scale application of named entity recognition to biomedicine and epidemiology.

ABSTRACT:

Background

Despite significant advancements in biomedical named entity recognition methods, the clinical application of these systems continues to face many challenges: (1) most of the methods are trained on a limited set of clinical entities; (2) these methods are heavily reliant on a large amount of data for both pre-training and prediction, making their use in production impractical; (3) they do not consider non-clinical entities, which are also related to patient's health, such as social, economic or demographic factors.

Methods

In this paper, we develop Bio-Epidemiology-NER (https://pypi.org/project/Bio-Epidemiology-NER/) an open-source Python package for detecting biomedical named entities from the text. This approach is based on a Transformer-based system and trained

SUBMITTER: Raza S 

PROVIDER: S-EPMC9931203 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature