Kajsa Tunedal

This paper develops a mathematical model of chylomicron dynamics incorporating the second meal effect, showing it can predict postprandial lipid responses across different BMI values.

This paper introduces a method to calculate uncertainty in model-derived biomarkers from non-normal hemodynamic data, identifying reliable biomarkers like ventricular relaxation rate.

The paper assesses the reproducibility of cardiovascular model parameters derived from 4D Flow MRI and cuff pressure measurements, finding variability ranges between 2.5% and 41% across different conditions.

This paper presents a hybrid digital twin model combining mechanistic and machine learning approaches to simulate ischemic stroke risk based on physiological factors over time.