Rune Sejer Jakobsen

Rune Sejer Jakobsen is a Postdoctoral Researcher and PhD student at the Department of Clinical Medicine, Faculty of Medicine, Aalborg University, Denmark. Affiliated with the Center for Clinical Data Science, he specializes in applying machine learning to clinical problems with a focus on hospital-acquired infections and risk stratification.

His research centers on developing explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infections (CAUTI), utilizing Bayesian networks and probabilistic modeling. Key interests include clinical decision support systems, innovation management in healthcare data science, and translating AI models into practical clinical tools for infection prevention and patient monitoring.

Recent publications (2023-2024) demonstrate a cohesive research trajectory: advancing explainable AI for hospital infection prediction, integrating machine learning with clinical workflows, and exploring innovation portfolios for AI implementation. His work bridges technical machine learning development with direct clinical applications in infection control.

Scientific Awards:

• No awards documented in available sources

As principal investigator of the 2019-2021 project 'Using probabilistic models in machine learning for risk stratification of hospital-acquired infections,' Jakobsen secured research funding for clinical AI development. He has not supervised students to date, consistent with his current PhD candidate status. Ongoing work involves expanding his Bayesian network frameworks to new clinical contexts and infection types.

He operates within Aalborg University's Center for Clinical Data Science, collaborating with clinical teams at Aalborg University Hospital to implement data-driven solutions for patient risk assessment and infection prevention in real-world healthcare settings.