Benjamin Skov Kaas-Hansen

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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.

Primary fields of research

• 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

Selected publications

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) In: Acta Anaesthesiologica Scandinavica. 8 p.

   Research output: Contribution to journal › Journal article › peer-review

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) In: Acta Anaesthesiologica Scandinavica. 10 p.

   Research output: Contribution to journal › Review › peer-review

3. Published

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

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

   Research output: Contribution to journal › Journal article › peer-review

4. Published

   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, In: npj Digital Medicine. 5, 10 p., 142.

   Research output: Contribution to journal › Journal article › peer-review

5. Published

   Pharmacovigilant Machine Learning in Big Data?

   Kaas-Hansen, Benjamin Skov, 2022

   Research output: Book/Report › Ph.D. thesis › Research

6. Published

   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, In: Clinical Epidemiology. 14, p. 213-223 11 p.

   Research output: Contribution to journal › Journal article › peer-review