Development and validation of MedDRA Tagger: a tool for extraction and structuring medical information from clinical notes.

ABSTRACT: Rapid and automated extraction of clinical information from patients' notes is a desirable though difficult task. Natural language processing (NLP) and machine learning have great potential to automate and accelerate such applications, but developing such models can require a large amount of labeled clinical text, which can be a slow and laborious process. To address this gap, we propose the MedDRA tagger, a fast annotation tool that makes use of industrial level libraries such as spaCy, biomedical ontologies and weak supervision to annotate and extract clinical concepts at scale. The tool can be used to annotate clinical text and obtain labels for training machine learning models and further refine the clinical concept extraction performance, or to extract clinical concepts for observatio

SUBMITTER: Humbert-Droz M 

PROVIDER: S-EPMC9774225 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature