INTERPRETATION OF DOPPLER BLOOD-FLOW VELOCITY WAVE-FORMS USING NEURAL NETWORKS

被引：0
|
作者：
BAYKAL, N
REGGIA, JA
YALABIK, N
ERKMEN, A
BEKSAC, MS
机构：
[1] UNIV MARYLAND,DEPT NEUROL,COLLEGE PK,MD 20742
[2] UNIV MARYLAND,INST ADV COMP STUDIES,COLLEGE PK,MD 20742
[3] MIDDLE E TECH UNIV,ANKARA,TURKEY
[4] HACETTEPE UNIV,SCH MED,ANKARA,TURKEY
来源：
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | 1994年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Doppler umbilical artery blood flow velocity waveform measurement is used in perinatal surveillance for the evaluation of pregnancy status. There is an ongoing debate on the predictive value of doppler measurements concerning the critical effect of the selection of parameters for the evaluation of doppler output. In this paper, we describe how neural network methods can be used both to discover relevant classification features and subsequently to classify patients. Classification accuracy varied from 92-99% correct.
引用
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页码：865 / 869
页数：5
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