Prediction of preterm infant mortality with Gaussian process classification

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

  • University of Helsinki

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
TilaJulkaistu - 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaEUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS, COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING - Bruges, Belgia
Kesto: 27 huhtikuuta 201629 huhtikuuta 2016
Konferenssinumero: 24

Conference

ConferenceEUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS, COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING
LyhennettäESANN
MaaBelgia
KaupunkiBruges
Ajanjakso27/04/201629/04/2016

ID: 15930717