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

  • 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

Udvalgte publikationer

  1. Udgivet

    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).

    Publikation: Working paperPreprintForskning

  2. 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 tidsskriftTidsskriftartikelfagfællebedømt

  3. Udgivet

    Pharmacovigilant Machine Learning in Big Data?

    Kaas-Hansen, Benjamin Skov, 2022

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

  4. 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 tidsskriftTidsskriftartikelfagfællebedømt

  5. Udgivet

    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, I: The Lancet Digital Health. 2, 4, s. e179–91 13 s.

    Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

ID: 185059892