Computer Science ›› 2016, Vol. 43 ›› Issue (Z11): 73-76, 82.doi: 10.11896/j.issn.1002-137X.2016.11A.016

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Estimation of Fetal Weight Based on Deep Neural Network

LI Kun, CHAI Yu-mei, ZHAO Hong-ling, ZHAO Yue-shu and NAN Xiao-fei   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Fetal weight is an important indicator which reflects the fetus’s growth and development status,so the estimation of fetal weight becomes a crucial foundation in obstetrical decision.Most traditional fetal weight prediction mo-dels are based on medical knowledge and feature selection,which are leading to the hard repetition and promotion of the model building process.For these problems,we proposed a deep neural network structure for building fetal weight prediction model,and introduced the process in which parameters are extracted from electronic health records and the fil-ling strategies for missing values.The experimental results show the deep neural network based prediction model outperforms traditional methods,and the filling strategy can reinforce the training of the model and improve the accuracy.Finally,the generalization ability and universality of the deep neural network model can help different areas and hospitals to build personalized fetal weight prediction model.

Key words: Fetal weight,Prediction model,Deep neural network

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