Detail

Keywords

Attitude to Computers; Automatic Data Processing; Body Temperature Changes; Computer Literacy; Data Interpretation, Statistical; Data Mining; Database Management Systems; Decision Making, Computer-Assisted; Decision Support Techniques; Diagnosis, Computer-Assisted; Drug Therapy, Computer-Assisted; Human-computer Interaction; Hypothermia; Statistics; Therapy, Computer-Assisted; Thermometers

Research group(s)

• AGD
  Head: Oliver Kimberger
  Research Area: Perioperative Data Science
  Members:
  Stefan Bartos
  Christoph Dibiasi
  Fabian Peter Hammerle
  Lorenz Kapral
  Oliver Kimberger
  Daniel Laxar
  Maleczek Mathias
  Clemens Pramhas
  Sebastian Zeiner

Research interests

My main research focus is the application/implementation of novel data processing technology in the field of perioperative data science for decision support, disease detection and prevention and - ultimately - establishment of personalized medicine via "virtual twin" concepts and also includes human/computer interaction. 

Additionally my research interests include hypothermia research (accidental and therapeutic),  methods for the conservation of normothermia and measurement of core temperature. 

Techniques, methods & infrastructure

Perioperative Data Science Lab with access to supercomputing, "big data" processing, data mining analytics, AR/VR methodology in collaboration with the LBI for Digital Health and Patient Safety. 

Selected publications

1. Maleczek, M. et al. (2024) “A Comparison of Five Algorithmic Methods and Machine Learning Pattern Recognition for Artifact Detection in Electronic Records of Five Different Vital Signs: A Retrospective Analysis,” Anesthesiology. Edited by , 141(1), pp. 32–43. Available at: https://doi.org/10.1097/aln.0000000000004971.
2. Laxar, D. et al. (2023) “The influence of explainable vs non-explainable clinical decision support systems on rapid triage decisions: a mixed methods study,” BMC Medicine. Edited by , 21(1). Available at: https://doi.org/10.1186/s12916-023-03068-2.
3. Bologheanu, R. et al. (2025) “New Persistent Opioid Use After Surgery,” JAMA Network Open. Edited by , 8(2), p. e2460794. Available at: https://doi.org/10.1001/jamanetworkopen.2024.60794.
4. Bologheanu, R. et al. (2023) “Development of a Reinforcement Learning Algorithm to Optimize Corticosteroid Therapy in Critically Ill Patients with Sepsis,” Journal of Clinical Medicine. Edited by , 12(4), p. 1513. Available at: https://doi.org/10.3390/jcm12041513.
5. Kapral, L. et al. (2024) “Development and external validation of temporal fusion transformer models for continuous intraoperative blood pressure forecasting,” eClinicalMedicine. Edited by , 75, p. 102797. Available at: https://doi.org/10.1016/j.eclinm.2024.102797.