Prediction of major complications affecting very low birth weight infants

被引:0
作者
Rinta-Koski, Olli-Pekka [1 ]
Sarkka, Simo [2 ]
Hollmen, Jaakko [1 ]
Leskinen, Markus [3 ,4 ]
Rantakari, Krista [3 ,4 ]
Andersson, Sture [3 ,4 ]
机构
[1] Aalto Univ, Dept Comp Sci, Espoo, Finland
[2] Aalto Univ, Dept Elect Engn & Automat, Espoo, Finland
[3] Univ Helsinki, Pediat Res Ctr, Childrens Hosp, Helsinki, Finland
[4] Helsinki Univ Hosp, Helsinki, Finland
来源
2017 IEEE LIFE SCIENCES CONFERENCE (LSC) | 2017年
关键词
biomedical time series analysis; Gaussian process classification; bronchopulmonary dysplasia; necrotizing enterocolitis; retinopathy of prematurity; neonatal intensive care; very low birth weight infants; HEART-RATE CHARACTERISTICS; BRONCHOPULMONARY DYSPLASIA; NECROTIZING ENTEROCOLITIS; PREMATURITY; RETINOPATHY; PATHOGENESIS; SEPSIS;
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Bronchopulmonary dysplasia (BPD), necrotizing enterocolitis (NEC), and retinopathy of prematurity (ROP) are severe complications affecting Very Low Birth Weight (VLBW) infants. Our findings show that data gathered in the intensive care unit during the first 24 or 72 hours of care can be used to predict whether a VLBW infant is at risk of developing BPD. Using Gaussian process classification, we achieved classification results with areas under the receiver operator characteristic curve of 0.85 (standard error (SE) 0.05) for 24h and 0.87 (SE 0.06) for 72h BPD data. This compares favourably with results achieved using the clinical standard SNAP-II and SNAPPE-II scores. Sensitivity for BPD was 0.52 (SE 0.06). Sensitivity for NEC and ROP was close to zero, suggesting that NEC and ROP can not be reliably predicted with this approach from our data set.
引用
收藏
页码:186 / 189
页数:4
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