TY  -  
T1  - Gaussian process classification for prediction of in-hospital mortality among preterm infants 
SN  -  /  
UR  - http://hdl.handle.net/10138/302053 
T3  -  
A1  - Rinta-Koski, Olli-Pekka; Särkkä, Simo; Hollmén, Jaakko; Leskinen, Markus; Andersson, Sture 
A2  -  
PB  -  
Y1  - 2018 
LA  - eng 
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 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 operatin... 
VO  -  
IS  -  
SP  -  
OP  -  
KW  - BABIES SCORE; BIRTH-WEIGHT INFANTS; CLINICAL RISK INDEX; CRIB-II; Gaussian process classification; MODELS; NEONATAL INTENSIVE-CARE; Neonatal intensive care; SEVERITY; SNAPPE-II; Time series prediction; Very low birth weight infants; 113 Computer and information sciences; 3123 Gynaecology and paediatrics; 3126 Surgery, anesthesiology, intensive care, radiology 
N1  -   
PP  -  
ER  -