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Natural language processing has huge potential in the medical domain whichrecently led to a lot of research in this field. However, a prerequisite ofsecure processing of medical documents, e.g., patient notes and clinicaltrials, is the proper de-identification of privacy-sensitive information. Inthis paper, we describe our NLNDE system, with which we participated in theMEDDOCAN competition, the medical document anonymization task of IberLEF 2019.We address the task of detecting and classifying protected health informationfrom Spanish data as a sequence-labeling problem and investigate differentembedding methods for our neural network. Despite dealing in a non-standardlanguage and domain setting, the NLNDE system achieves promising results in thecompetition.

NLNDE: The Neither-Language-Nor-Domain-Experts' Way of Spanish Medical Document De-Identification