Prediction of preterm infant mortality with Gaussian process classification

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

Standard

Prediction of preterm infant mortality with Gaussian process classification. / Rinta-Koski, Olli-Pekka; Särkkä, Simo; Hollmen, Jaakko; Andersson, Sture.

Proceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2017. p. 193-198.

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

Harvard

Rinta-Koski, O-P, Särkkä, S, Hollmen, J & Andersson, S 2017, Prediction of preterm infant mortality with Gaussian process classification. in Proceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 193-198, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 27/04/2016.

APA

Rinta-Koski, O-P., Särkkä, S., Hollmen, J., & Andersson, S. (2017). Prediction of preterm infant mortality with Gaussian process classification. In Proceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 193-198)

Vancouver

Rinta-Koski O-P, Särkkä S, Hollmen J, Andersson S. Prediction of preterm infant mortality with Gaussian process classification. In Proceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2017. p. 193-198

Author

Rinta-Koski, Olli-Pekka ; Särkkä, Simo ; Hollmen, Jaakko ; Andersson, Sture. / Prediction of preterm infant mortality with Gaussian process classification. Proceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2017. pp. 193-198

Bibtex - Download

@inproceedings{e0568095fea140fcb07b7eb494f394c4,
title = "Prediction of preterm infant mortality with Gaussian process classification",
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.",
author = "Olli-Pekka Rinta-Koski and Simo S{\"a}rkk{\"a} and Jaakko Hollmen and Sture Andersson",
year = "2017",
language = "English",
isbn = "9782875870391",
pages = "193--198",
booktitle = "Proceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

}

RIS - Download

TY - GEN

T1 - Prediction of preterm infant mortality with Gaussian process classification

AU - Rinta-Koski, Olli-Pekka

AU - Särkkä, Simo

AU - Hollmen, Jaakko

AU - Andersson, Sture

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

M3 - Conference contribution

SN - 9782875870391

SP - 193

EP - 198

BT - Proceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

ER -

ID: 15930717