@misc{16616,
  keywords = {Machine learning, transplantation, personalized medicine},
  author = {Andrea Stor{\r a}s and Anders {\r A}sberg and P{\r a}l Halvorsen and Michael Riegler and Inga Str{\"u}mke},
  title = {Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning},
  abstract = {Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that can accurately estimate the drug exposure for individual dose adaptions would be of high clinical value. In this work, we propose a new technique using machine learning to estimate the tacrolimus exposure in kidney transplant recipients. Our models achieve predictive errors that are at the same level as an established population pharmacokinetic model, but are faster to develop and require less knowledge about the pharmacokinetic properties of the drug.},
  year = {2022},
  journal = {35th IEEE CBMS International Symposium on Computer-Based Medical Systems},
  pages = {38-43},
  publisher = {IEEE},
  doi = {10.1109/CBMS55023.2022.00014},
}