Prediction of preterm infant mortality with Gaussian process classification

We present a method for predicting preterm infant inhospital-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.

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