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.

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 paper › Preprint › Forskning

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 tidsskrift › Tidsskriftartikel › fagfællebedømt

3. Udgivet

   Pharmacovigilant Machine Learning in Big Data?

   Kaas-Hansen, Benjamin Skov, 2022

   Publikation: Bog/antologi/afhandling/rapport › Ph.d.-afhandling › Forskning

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 tidsskrift › Tidsskriftartikel › fagfællebedømt

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 tidsskrift › Tidsskriftartikel › fagfællebedømt