Benjamin Skov Kaas-Hansen

Benjamin Skov Kaas-Hansen


Benjamin is a hybrid medical doctor and data scientist with an MSc in epidemiology and biostatistics and a PhD in pharmacovigilance and health informatics from University of Copenhagen. He holds a position as research fellow at Dep. of Intensive Care at Copenhagen University Hospital - Rigshospitalet. His scientific interests include in causal inference, platform/adaptive trial design, Bayesian methods, machine learning, and data visualisation and standardisation; R fluent, proficient in Python and SQL, and learning Julia.

Primære forskningsområder

  • Real-world data evidence
  • Platform/adaptive trial design
  • Machine learning in electronic patient records
  • Bayesian analysis and machine learning in epidemiology
  • Data standardisation and visualisation

Udvalgte publikationer

  1. E-pub ahead of print

    Health-related quality of life trajectories in critical illness: Protocol for a Monte Carlo simulation study

    Kaas-Hansen, Benjamin Skov, Kjaer, M. N., Møller, Morten Hylander, Jensen, Aksel Karl Georg, Larsen, M. E., Cuthbertson, B. H., Perner, Anders & Granholm, A., 2023, (E-pub ahead of print) I: Acta Anaesthesiologica Scandinavica. 8 s.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  2. E-pub ahead of print

    Real-world causal evidence for planned predictive enrichment in critical care trials: A scoping review

    Kaas-Hansen, Benjamin Skov, Granholm, A., Sivapalan, P., Anthon, C. T., Schjørring, O. L., Maagaard, M., Kjaer, M. N., Mølgaard, J., Ellekjaer, K. L., Fagerberg, S. K., Lange, Theis, Møller, Morten Hylander & Perner, Anders, 2023, (E-pub ahead of print) I: Acta Anaesthesiologica Scandinavica. 10 s.

    Publikation: Bidrag til tidsskriftReviewForskningfagfællebedømt

  3. Udgivet

    adaptr: an R package for simulating and comparing adaptive clinical trials

    Granholm, A., Jensen, Aksel Karl Georg, Lange, Theis & Kaas-Hansen, Benjamin Skov, 2022, I: Journal of Open Source Software. 7, 72, 1 s., 4284.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  4. Udgivet

    Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data

    Thorsen-Meyer, H., Placido, Davide, Kaas-Hansen, Benjamin Skov, Nielsen, A. P., Lange, Theis, Nielsen, Annelaura Bach, Toft, P., Schierbeck, J., Strøm, T., Chmura, Piotr Jaroslaw, Heimann, M., Belling, K., Perner, Anders & Brunak, Søren, 2022, I: npj Digital Medicine. 5, 10 s., 142.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  5. Udgivet

    Pharmacovigilant Machine Learning in Big Data?

    Kaas-Hansen, Benjamin Skov, 2022

    Publikation: Bog/antologi/afhandling/rapportPh.d.-afhandlingForskning

  6. Udgivet

    Using Machine Learning to Identify Patients at High Risk of Inappropriate Drug Dosing in Periods with Renal Dysfunction

    Kaas-Hansen, Benjamin Skov, Leal Rodríguez, C., Placido, Davide, Thorsen-Meyer, H., Nielsen, A. P., Dérian, N., Brunak, Søren & Andersen, Stig Ejdrup, 2022, I: Clinical Epidemiology. 14, s. 213-223 11 s.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

ID: 185059892