Sepsis is a serious, life-threatening condition that presents a growing problem in medicine and health-care. It is characterized by quick progression and high variability in the disease manifestation, so treatment should be personalized and tailored to fit individual characteristics of a particular subject. That requires close monitoring of the patient's state and reliable predictions of how the targeted therapy will affect sepsis progression over time. We have characterized predictive capabilities of a graph-based structured regression approach under hemoadsorption therapy by using a computational model of sepsis biomarker progression in rats. Results suggests that an extension of the model representational power by using a dense graph and multiple-step predictors increases predictive accuracy, allowing more appropriate choice of treatment.