GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines
This addresses the low-resource problem for German medical NLP applications, enabling research without data protection restrictions, though it is incremental as it applies existing methods to new data.
The authors tackled the lack of publicly accessible German medical text corpora by creating GGPONC, a freely distributable corpus based on clinical practice guidelines for oncology, which is one of the largest of its kind and includes rich metadata like literature references and evidence levels.
The lack of publicly accessible text corpora is a major obstacle for progress in natural language processing. For medical applications, unfortunately, all language communities other than English are low-resourced. In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely distributable German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions. Moreover, GGPONC is the first corpus for the German language covering diverse conditions in a large medical subfield and provides a variety of metadata, such as literature references and evidence levels. By applying and evaluating existing medical information extraction pipelines for German text, we are able to draw comparisons for the use of medical language to other corpora, medical and non-medical ones.