@article{KatzensteinerLudwigMarscholleketal.2019,
  author    = {Matthias Katzensteiner and Wolfram Ludwig and Michael Marschollek and Oliver J. Bott},
  title     = {Results of a Literature Review to Prepare Data Modelling in the Context of Kidney Transplant Rejection Diagnosis},
  series   = {ICT for Health Science Research (Studies in Health Technology and Informatics ; 258)},
  doi       = {10.25968/opus-1634},
  url       = {http://nbn-resolving.de/urn:nbn:de:bsz:960-opus4-16347},
  pages     = {179 -- 183},
  year      = {2019},
  abstract  = {Due to demographic change the number of serious kidney diseases and thus required transplantations will increase. The increased demand for donor organs and a decreasing supply of these organs underline the necessity for effective early rejection diagnostic measures to improve the lifetime of transplants. Expert systems might improve rejection diagnostics but for the development of such systems data models are needed that encompass the relevant information to enable optimal data aggregation and evaluation. Results of a literature review concerning published data models and information systems concerned with kidney transplant rejection diagnostic lead to a set of data elements even if no papers could be identified that publish data models explicitly.},
  language  = {en}
}