Best Paper Award at the SITB'23 symposium

At the 43rd Symposium on Information Theory and Signal Processing in the Benelux, our MSc student Alan Hamo has won the award for Best Student Presentation, for the paper "Machine learning algorithm to predict cardiac output based on arterial pressure measurement". Congratulations!

Paper abstract

Cardiac output (CO) plays a crucial role in determining the delivery of oxygen to tissues and is a key metric in hemodynamic optimization. The gold standard method for measuring cardiac output is through thermodilution using pulmonary artery catheter, but it is an invasive procedure associated with complications during placement and the need for a skilled expert to perform the measurements. An alternative approach is to estimate cardiac output by utilizing arterial blood pressure (ABP) measurements, which is a minimally invasive technique. However, the relationship between ABP and CO is not yet fully understood. In this study, we aim to utilize regression-based machine learning techniques and feature engineering to estimate CO from ABP. Hemodynamics and waveform features, along with demographic information of the patient, were integrated to enhance the  accuracy of the model.

MSc thesis supervisor: Justin Dauwels. Joint work with Drs. Niki Ottenhof and Jan-Wiebe Korstanje of the Erasmus MC. 

More ...