Schapranow, M.-P., Borchert, F., Bougatf, N., Hund, H., Eils, R.: Software-Tool Support for Collaborative, Virtual, Multi-Site Molecular Tumor Boards. SN Computer Science. 4, 358 (2023).
Ladas, N., Borchert, F., Franz, S., Rehberg, A., Strauch, N., Sommer, K.K., Marschollek, M., Gietzelt, M.: Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts. Health Informatics Journal. 29, 14604582231164696 (2023).
Bressem, K.K., Papaioannou, J.-M., Grundmann, P., Borchert, F., Adams, L.C., Liu, L., Busch, F., Xu, L., Loyen, J.P., Niehues, S.M., Augustin, M., Grosser, L., Makowski, M.R., Aerts, H.J., Löser, A.: MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain, https://arxiv.org/abs/2303.08179, (2023).
Richter-Pechanski, P., Wiesenbach, P., Schwab, D.M., Kiriakou, C., He, M., Allers, M.M., Tiefenbacher, A.S., Kunz, N., Martynova, A., Spiller, N., Mierisch, J., Borchert, F., Schwind, C., Frey, N., Dieterich, C., Geis, N.A.: A Distributable German Clinical Corpus Containing Cardiovascular Clinical Routine Doctor’s Letters. Scientific Data. 10, 207 (2023).
We present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor's letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes. To the best of our knowledge, CARDIO:DE is the first freely available and distributable German clinical corpus in the cardiovascular domain. In summary, our corpus offers unique opportunities for collaborative and reproducible research on natural language processing models for German clinical texts.