  We present a statistical model for German medical natural language processing
trained for named entity recognition (NER) as an open, publicly available
model. The work serves as a refined successor to our first GERNERMED model
which is substantially outperformed by our work. We demonstrate the
effectiveness of combining multiple techniques in order to achieve strong
results in entity recognition performance by the means of transfer-learning on
pretrained deep language models (LM), word-alignment and neural machine
translation. Due to the sparse situation on open, public medical entity
recognition models for German texts, this work offers benefits to the German
research community on medical NLP as a baseline model. Since our model is based
on public English data, its weights are provided without legal restrictions on
usage and distribution. The sample code and the statistical model is available
at: https://github.com/frankkramer-lab/GERNERMED-pp

Computation and Language;Artificial Intelligence;Machine Learning