Automatic extraction of 12 cardiovascular concepts from German discharge letters using pre-trained language models.

ABSTRACT:

Objective

A vast amount of medical data is still stored in unstructured text documents. We present an automated method of information extraction from German unstructured clinical routine data from the cardiology domain enabling their usage in state-of-the-art data-driven deep learning projects.

Methods

We evaluated pre-trained language models to extract a set of 12 cardiovascular concepts in German discharge letters. We compared three bidirectional encoder representations from transformers pre-trained on different corpora and fine-tuned them on the task of cardiovascular concept extraction using 204 discharge letters manually annotated by cardiologists at the University Hospital Heidelberg. We compared our results with traditional machine learning methods based on a long sho

SUBMITTER: Richter-Pechanski P 

PROVIDER: S-EPMC8637713 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature