Temporal Annotation of German Clinical Language in Real and Synthetic Clinical Documents: Corpus Development and Baseline Tagger Validation Study.

backgroundTemporal information about patients constitutes a precious source for clinical decision-making and medical treatment. The automatic extraction of such data from unstructured clinical narratives requires time-annotated clinical reports and notes from which time-informed taggers can be learned. Unfortunately, the non-English clinical language community, the German one as a typical example, with only a few exceptions, generally lacks such time-annotated resources to train and evaluate temporal taggers.

objectiveTo overcome this metadata bottleneck, we developed a TimeML-conformant annotation schema for both temporal entities and temporal relations adapted to the needs of German medical language. Based on the annotations derived therefrom, we trained state-of-the-art baseline taggers to recognize temporal expressions in clinical documents.

methodsStarting from temporal annotation guidelines for English clinical documents, we developed preliminary annotation guidelines for temporal named entities and temporal relations for the German language. These guidelines were subsequently refined and adapted to German clinical jargon, incorporating the work experience of 5 clinically trained annotators (students of medicine). For this task, we used randomly selected smaller subsets of 2 German clinical corpora-a real-world one (3000PA

resultsWe created 3000PA

conclusionsWe here introduce the first TimeML-compliant annotation scheme for time expressions occurring in German clinical language and apply it to 2 clinical corpora, one with nondistributable real clinical data, the other with distributable synthetic ones. The latter constitutes the first publicly accessible, temporally annotated clinical corpus for the German language. The time tagger trained on these datasets is the first of its kind, fully compliant with the TimeML markup language. The amounts of temporal metadata in our corpora are among the largest datasets ever produced for the clinical domain, both compared with English and German predecessors.