Optimized OR utilization with artificial intelligence
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  1. 1. Place of first publication: Ramolla/Jürgensen, Optimierte Saalauslastung.
    OP-Management up2date 2023; 03(01): 77-88 DOI: 10.1055/a-1992-9076
    © 2023 Thieme
    Ramolla/Jürgensen, Optimized OR utilisation.
    OR-Management up2date 2023; 03(01): 77-88 DOI: 10.1055/a-1992-9076
    © 2023 Thieme

  2. 2. Zaubitzer L, Affolter A, Büttner S et al. Zeitmanagement im OP – eine Querschnittstudie zur Bewertung der subjektiven und objektiven Dauer chirurgischer Prozeduren im HNO-Bereich. HNO 2022; 70: 436–444

  3. 3. Gomes C, Almada-Lobo B, Borges J et al. Integrating Data Mining and Optimization Techniques on Surgery Schedu- ling. In: Zhou S, Zhang S, Karypis G (eds.) Advanced data mining and applications ADMA 2012. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2012: doi:10.1007/978-3-642-35527-1_49

  4. 4. Chen T, Guestrin C. Association for Computing Machinery.
    XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge
    Discovery and Data Mining (KDD '16). Introduction to Boosted Trees — xgboost 1.7.1 documentation; New York,
    NY, USA: 2016: doi:10.1145/2939672.2939785

  5. 5. Shwartz-Ziv R, Armon A. Tabular data: Deep learning is not all you need, 8th ICMLWorkshop on Automated Machine Learning. Zugriff am 08. Dezember 2022: https://openreview.
    net/attachment?id=vdgtepS1pV&name=original_version 2021)