Evaluation of Urinary S100B Protein Level and Lactate/Creatinine Ratio for Early Diagnosis and Prognostic Prediction of Neonatal Hypoxic-Ischemic Encephalopathy

Neonatology ◽  
2010 ◽  
Vol 97 (1) ◽  
pp. 41-44 ◽  
Author(s):  
Li Liu ◽  
Chong-xun Zheng ◽  
Shu-feng Peng ◽  
Hong-yan Zhou ◽  
Zu-you Su ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Protein Level ◽  
Hypoxic Ischemic Encephalopathy ◽  
S100b Protein ◽  
Creatinine Ratio ◽  
Prognostic Prediction ◽  
Ischemic Encephalopathy

scholarly journals Association between Urinary Lactate to Creatinine Ratio and Neurodevelopmental Outcome in Term Infants with Hypoxic-Ischemic Encephalopathy

2008 ◽  
Vol 153 (3) ◽  
pp. 375-378.e2 ◽  
Author(s):  
William Oh ◽  
Rebecca Perritt ◽  
Seetha Shankaran ◽  
Matthew Merritts ◽  
Edward F. Donovan ◽  
...  
Keyword(s):  
Neurodevelopmental Outcome ◽  
Hypoxic Ischemic Encephalopathy ◽  
Creatinine Ratio ◽  
Term Infants ◽  
Ischemic Encephalopathy

scholarly journals The Use of Fuzzy BackPropagation Neural Networks for the Early Diagnosis of Hypoxic Ischemic Encephalopathy in Newborns

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Liu Li ◽  
Huo Liqing ◽  
Lu Hongru ◽  
Zhang Feng ◽  
Zheng Chongxun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Early Diagnosis ◽  
Recognition Rate ◽  
Hypoxic Ischemic Encephalopathy ◽  
Correct Recognition ◽  
Backpropagation Neural Networks ◽  
Artificial Neural ◽  
Correct Recognition Rate ◽  
Ischemic Encephalopathy

Objective. To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility.Methods. Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE and employed in the present study, which incorporates fuzzy logic with artificial neural networks.Results. The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%.Conclusion. A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. Therefore, this method provides a convenient tool for the early clinical diagnosis of HIE.

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