Journal
NEUROCOMPUTING
Volume 298, Issue -, Pages 134-141Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2017.12.064
Keywords
Time series prediction; Gaussian process classification; Very low birth weight infants; Neonatal intensive care
Categories
Funding
- Academy of Finland [295505, 266940]
- Academy of Finland (AKA) [295505, 266940, 295505, 266940] Funding Source: Academy of Finland (AKA)
Ask authors/readers for more resources
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. (C) 2018 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available