Prediction of preterm infant mortality with Gaussian process classification

Olli-Pekka Rinta-Koski; Simo Särkkä; Jaakko Hollmen; Sture Andersson

Prediction of preterm infant mortality with Gaussian process classification

Olli-Pekka Rinta-Koski, Simo Särkkä, Jaakko Hollmen, Sture Andersson

University of Helsinki

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Kustantaja	European Symposium on Artificial Neural Networks (ESANN)
Sivut	193-198
ISBN (painettu)	9782875870391
Tila	Julkaistu - 2017
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	EUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS, COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING - Bruges, Belgia Kesto: 27 huhtik. 2016 → 29 huhtik. 2016 Konferenssinumero: 24

Conference

Conference	EUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS, COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING
Lyhennettä	ESANN
Maa/Alue	Belgia
Kaupunki	Bruges
Ajanjakso	27/04/2016 → 29/04/2016

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@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",

publisher = "European Symposium on Artificial Neural Networks (ESANN)",

note = " European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN ; Conference date: 27-04-2016 Through 29-04-2016",

}

Rinta-Koski, O-P, Särkkä, S , Hollmen, J & Andersson, S 2017, Prediction of preterm infant mortality with Gaussian process classification. julkaisussa Proceedings of 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. European Symposium on Artificial Neural Networks (ESANN), Sivut 193-198, EUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS, COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING, Bruges, Belgia, 27/04/2016.

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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

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

N1 - Conference code: 24

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 article in proceedings

SN - 9782875870391

SP - 193

EP - 198

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

PB - European Symposium on Artificial Neural Networks (ESANN)

T2 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Y2 - 27 April 2016 through 29 April 2016

ER -

Prediction of preterm infant mortality with Gaussian process classification

Abstrakti

Conference

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Science-IT

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