Benjamin Skov Kaas-Hansen

Benjamin Skov Kaas-Hansen

PhD student, Assistant Lecturer

Benjamin is a hybrid medical doctor and data scientist with an MSc in epidemiology and biostatistics, currently pursuing a PhD in clinical pharmacology and medical informatics at University of Copenhagen. The aim of his research is to leverage longitudinal electronic medical records and registry data to give actionable answers to substantial pharmacovigilance questions. He is interested in causal inference, Bayesian methods, machine learning, and data visualisation and standardisation; R fluent, proficient in Python and SQL, and learning Julia.

Primary fields of research

- Pharmacovigilance (focus on polypharmacy)

- Data and text mining in electronic patient records

- Applying Bayesian and machine learning data analytic tools in epidemiology

- Data standardisation

Selected publications

  1. 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, A. B., Nielsen, Anna Pors, Kaas-Hansen, Benjamin Skov, Toft, P., Schierbeck, J., Strøm, T., Chmura, Piotr Jaroslaw, Heimann, M., Dybdahl, L., Spangsege, L., Hulsen, P., Belling, Kirstine G, Brunak, Søren & Perner, Anders, 2020, In: The Lancet Digital Health. 2, 4, p. e179–91 13 p.

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Published

    Heterogeneity of treatment effect of prophylactic pantoprazole in adult ICU patients: a post hoc analysis of the SUP-ICU trial

    Granholm, A., Marker, S., Krag, M., Zampieri, F. G., Thorsen-Meyer, H., Kaas-Hansen, Benjamin Skov, van der Horst, I. C. C., Lange, Theis, Wetterslev, J., Perner, Anders & Møller, Morten Hylander, 2020, In: Intensive Care Medicine. 46, p. 717–726 10 p.

    Research output: Contribution to journalJournal articleResearchpeer-review

ID: 185059892