Gaussian process classification for prediction of in-hospital mortality among preterm infants
Research output: Contribution to journal › Article › Scientific › peer-review
- University of Helsinki
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 72 h of care for 598 Very Low Birth Weight infants of birth weight < 1500 g, with standard clinical features calculated on arrival at the Neonatal Intensive Care Unit. Time periods of 12, 18, 24, 36, 48, and 72 h were evaluated. We achieved a classification result with area under the receiver operating characteristic curve of 0.948, which is in excess of the results achieved by using the clinical standard SNAP-II and SNAPPE-II scores.
|Publication status||Published - 2018|
|MoE publication type||A1 Journal article-refereed|
- Gaussian process classification, Neonatal intensive care, Time series prediction, Very low birth weight infants