Evaluating Performance and Interpretability of Machine Learning Methods for Predicting Delirium in Gerontopsychiatric Patients

Michael Netzer, Werner O. Hackl, Michael Schaller, Lisa Alber, Josef Marksteiner, Elske Ammenwerth. Evaluating Performance and Interpretability of Machine Learning Methods for Predicting Delirium in Gerontopsychiatric Patients. In Schreier Günter, Hayn Dieter, Eggerth Alphons, editors, dHealth 2020 - Biomedical Informatics for Health and Care - Proceedings of the 14th Health Informatics Meets Digital Health Conference, Vienna, Austria, 19-20 May 2020. Volume 271 of Studies in Health Technology and Informatics, pages 121-128, IOS Press, 2020. [doi]

@inproceedings{NetzerHSAMA20,
  title = {Evaluating Performance and Interpretability of Machine Learning Methods for Predicting Delirium in Gerontopsychiatric Patients},
  author = {Michael Netzer and Werner O. Hackl and Michael Schaller and Lisa Alber and Josef Marksteiner and Elske Ammenwerth},
  year = {2020},
  doi = {10.3233/SHTI200087},
  url = {https://doi.org/10.3233/SHTI200087},
  researchr = {https://researchr.org/publication/NetzerHSAMA20},
  cites = {0},
  citedby = {0},
  pages = {121-128},
  booktitle = {dHealth 2020 - Biomedical Informatics for Health and Care - Proceedings of the 14th Health Informatics Meets Digital Health Conference, Vienna, Austria, 19-20 May 2020},
  editor = {Schreier Günter and Hayn Dieter and Eggerth Alphons},
  volume = {271},
  series = {Studies in Health Technology and Informatics},
  publisher = {IOS Press},
  isbn = {978-1-64368-085-9},
}