GERNERMED -- An Open German Medical NER Model

TL;DR

GERNERMED is the first open-source neural NLP model designed for German medical named entity recognition, trained on translated datasets to balance data privacy with model accessibility.

Contribution

It introduces a novel open German medical NER model trained on translated datasets, addressing data privacy concerns in medical NLP.

Findings

First open German medical NER model

Achieves effective entity recognition in German medical texts

Available for public use and further research

Abstract

The current state of adoption of well-structured electronic health records and integration of digital methods for storing medical patient data in structured formats can often considered as inferior compared to the use of traditional, unstructured text based patient data documentation. Data mining in the field of medical data analysis often needs to rely solely on processing of unstructured data to retrieve relevant data. In natural language processing (NLP), statistical models have been shown successful in various tasks like part-of-speech tagging, relation extraction (RE) and named entity recognition (NER). In this work, we present GERNERMED, the first open, neural NLP model for NER tasks dedicated to detect medical entity types in German text data. Here, we avoid the conflicting goals of protection of sensitive patient data from training data extraction and the publication of the statistical model weights by training our model on a custom dataset that was translated from publicly available datasets in foreign language by a pretrained neural machine translation model. The sample code and the statistical model is available at: https://github.com/frankkramer-lab/GERNERMED