Prediction of preterm infant mortality with Gaussian process classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • University of Helsinki

Abstract

We present a method for predicting preterm infant in-hospital-mortality using Bayesian Gaussian process classification. We combined features extracted from sensor measurements, made during the first 24 hours of care for 581 Very Low Birth Weight infants, with standard clinical features calculated on arrival at the Neonatal Intensive Care Unit. We achieved a classification result with area under curve of 0.94 (standard error 0.02), which is in excess of the results achieved by using the clinical standard SNAP-II and SNAPPE-II scores.

Details

Original languageEnglish
Title of host publicationProceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
Event European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium
Duration: 27 Apr 201629 Apr 2016
Conference number: 24

Conference

Conference European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated titleESANN
CountryBelgium
CityBruges
Period27/04/201629/04/2016

ID: 15930717