Machine learning in neonatal intensive care

Olli-Pekka Rinta-Koski

Research output: ThesisDoctoral ThesisCollection of Articles


This thesis deals with the applications of machine learning in the context of neonatal intensive care. The main focus is on prediction using multichannel time series data. The purpose of developing prediction methods is to create data-driven tools for helping care personnel to make more informed decisions.  Preterm infants, born before 37 weeks of gestation, are subject to many developmental issues and health problems. Very Low Birth Weight 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. Neonatal intensive care requires combining information from multiple sources, often with severe and far-reaching consequences for the patient. Real-time data is available on multiple monitor screens in the ward, and is used by care personnel to help them make informed decisions. Analysis of trends and vast data sets, possibly spanning thousands of patients, requires the use of machine learning methods.  In the research done for this thesis we have applied machine learning methods, in particular Gaussian process classification, 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.
Translated title of the contributionKoneoppiminen vastasyntyneiden tehohoidossa
Original languageEnglish
QualificationDoctor's degree
Awarding Institution
  • Aalto University
  • Särkkä, Simo, Supervising Professor
  • Hollmen, Jaakko, Thesis Advisor
Print ISBNs978-952-60-8209-7
Electronic ISBNs978-952-60-8210-3
Publication statusPublished - 2018
MoE publication typeG5 Doctoral dissertation (article)


  • machine learning
  • neonatology


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