De-Identification of German Medical Admission Notes

Medical texts are a vast resource for medical and computational research. In contrast to newswire or wikipedia texts medical texts need to be de-identified before making them accessible to a wider NLP research community. We created a prototype for German medical text de-identification and named entity recognition using a three-step approach. First, we used well known rule-based models based on regular expressions and gazetteers, second we used a spelling variant detector based on Levenshtein distance, exploiting the fact that the medical texts contain semi-structured headers including sensible personal data, and third we trained a named entity recognition model on out of domain data to add statistical capabilities to our prototype. Using a baseline based on regular expressions and gazetteers we could improve F2-score from 78% to 85% for de-identification. Our prototype is a first step for further research on German medical text de-identification and could show that using spelling variant detection and out of domain trained statistical models can improve de-identification performance significantly.