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

• Pharmacovigilance
• Causal inference and platform/adaptive trial design
• Data and text mining in electronic patient records
• Bayesian analysis and machine learning in epidemiology
• Data standardisation and visualisation

Selected publications

1. Published

   Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records

   Kaas-Hansen, Benjamin Skov, Placido, Davide, Rodrìguez, C. L., Thorsen-Meyer, H., Gentile, S., Nielsen, A. P., Brunak, Søren, Jürgens, Gesche & Andersen, Stig Ejdrup, 2022, Authorea, (Authorea Preprints).

   Research output: Working paper › Preprint › Research

2. 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

3. Published

   Pharmacovigilant Machine Learning in Big Data?

   Kaas-Hansen, Benjamin Skov, 2022

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

4. 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

5. Published

   Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records

   Thorsen-Meyer, H., Nielsen, Annelaura Bach, Nielsen, A. P., Kaas-Hansen, Benjamin Skov, Toft, P., Schierbeck, J., Strøm, T., Chmura, Piotr Jaroslaw, Heimann, M., Dybdahl, L., Spangsege, L., Hulsen, P., Belling, K., Brunak, Søren & Perner, Anders, 2020, In: The Lancet Digital Health. 2, 4, p. e179–91 13 p.

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