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

Previous Articles     Next Articles

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

[1] 刘致君,李桂荣,郭兴巧.预测胎儿体重新方法与传统方法的比较[J].中国妇幼保健,2008,23(24):3478-3479
[2] 唐慧霞,李胜利.超声估测胎儿体重的研究进展[J].中华医学超声杂志 (电子版),2014,11(5):9-14
[3] Merz E,Lieser H,Schicketanz K H,et al.Intrauterine fetalweight assessment using ultrasound.A comparison of several weight assessment methods and development of a new formula for the determination of fetal weight[J].Ultraschall in der Medizin (Stuttgart,Germany:1980),1988,9(1):15-24
[4] Schild R L,Sachs C,Fimmers R,et al.Sex-specific fetal weight prediction by ultrasound[J].Ultrasound in Obstetrics & Gynecology,2004,23(1):30-35
[5] Farmer R M,Medearis A L,Hirata G I,et al.The use of a neural network for the ultrasonographic estimation of fetal weight in the macrosomicfetus[J].American Journal of Obstetrics and Gynecology,1992,166(5):1467-1472
[6] Cheng Y C,Hsia C C,Chang F M,et al.Cluster-Based Artificial Neural Network on Ultrasonographic Parameters for Fetal Weight Estimation[C]∥6th World Congress of Biomechanics (WCB 2010).2010 Singapore.Springer Berlin Heidelberg,2010:1514-1517
[7] Cheng Y C,Chiu Y H,Wang H C,et al.Using Akaike information criterion and minimum mean square error mode in compensating for ultrasonographic errors for estimation of fetal weight by new operators[J].Taiwanese Journal of Obstetrics and Gynecology,2013,52(1):46-52
[8] Mohammadi H,Nemati M,Allahmoradi Z,et al.Ultrasound estimation of fetal weight in twins by artificial neural network[J].Journal of Biomedical Science and Engineering,2011,4(1):46
[9] Hinton G E,Salakhutdinov R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507
[10] Hinton G,Deng L,Yu D,et al.Deep neural networks for acous-tic modeling in speech recognition:The shared views of four research groups[J].IEEE Signal Processing Magazine,2012,29(6):82-97
[11] 王坚,张媛媛.基于深度神经网络的汉语语音合成的研究[J].计算机科学,2015,42(6A):75-78
[12] Krizhevsky A,Sutskever I,Hinton G E.Imagenet classification with deep convolutional neural networks[C]∥Advances in Neural Information Processing Systems.2012,25(2):1097-1105
[13] 王莹,樊鑫,李豪杰,等.基于深度网络的多形态人脸识别[J].计算机科学,2015,42(9):61-65
[14] 李海朋,李晶皎,闫爱云,等.人脸识别中的遗传神经网络并行实现[J].计算机科学,2015,42(6A):168-174
[15] Lipton Z C,Kale D C,Elkan C,et al.Learning to Diagnose with LSTM Recurrent Neural Networks[J].Computer Science ,2015
[16] 孙志远,鲁成祥,史忠植,马刚.深度学习研究与进展[J].计算机科学,2016,43(2):1-8
[17] Uzuner ,Luo Y,Szolovits P.Evaluating the state-of-the-art in automatic de-identification[J].Journal of the American Medical Informatics Association,2007,14(5):550-563
[18] Stubbs A,Uzuner .Annotating longitudinal clinical narratives for de-identification:The 2014 i2b2/UTHealthcorpus[J].Journal of Biomedical Informatics,2015,58:S20-S29
[19] Stubbs A,Kotfila C,Uzuner .Automated systems for the de-identification of longitudinal clinical narratives:Overview of 2014 i2b2/UTHealth shared task Track 1[J].Journal of Biomedical Informatics,2015,58:S11-S19
[20] Srivastava N,Hinton G,Krizhevsky A,et al.Dropout:A simple way to prevent neural networks from overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .