GERNERMED -- An Open German Medical NER Model
This provides a tool for medical data analysis in German, addressing privacy concerns by using translated public data, but it is incremental as it adapts existing methods to a new language domain.
The authors tackled the problem of extracting medical entities from unstructured German text by developing GERNERMED, the first open neural NLP model for German medical NER, achieving performance comparable to existing models with an F1-score of 0.85 on their custom dataset.
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