Jaakko Hollmén, Stockholm University, Department of Computer and Systems Sciences, Sweden
Jaakko Hollmén is a faculty member at Department of Computer and Systems Sciences at Stokcholm University in Sweden (since September 2019). Prior to joining Stokcholm university, he was a faculty member at the Department of Computer Science at Aalto University in Finland. His research interests include theory and practice of machine learning and data mining, in particular in the context of health, medicine and environmental sciences. He has been involved in the organization of many IDA conferences for the past ten years. He is also the secretary of the IDA council.
Title of Talk: Diagnostic prediction in neonatal intensive care units
Abstract: Preterm infants, born before 37 weeks of gestation, are subject to many developmental issues and health problems. Very Low Birth Weight (VLBW) infants, with a birth weight under 1500 g, are the most afflicted in this group. These infants require treatment in the neonatal intensive care unit before they are mature enough for hospital discharge. The neonatal intensive care unit is a data-intensive environment, where multi-channel physiological data is gathered from patients using a number of sensors to construct a comprehensive picture of the patients’ vital signs. We have looked into the problem how to predict neonatal in-hospital mortality and morbidities. We have used time series data collected from Very Low Birth Weight infants treated in the neonatal intensive care unit of Helsinki University Hospital between 1999 and 2013. Our results show that machine learning models based on time series data alone have predictive power comparable with standard medical scores, and combining the two results in improved predictive ability. We have also studied the effect of observer bias on recording vital sign measurements in the neonatal intensive care unit, as well as conducted a retrospective cohort study on trends in the growth of Extremely Low Birth Weight (birth weight under 1000 g) infants during intensive care.