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计算机系统应用:2020,29(3):39-46
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基于变长时间间隔LSTM方法的胎儿异常体重预测
(1.杭州师范大学 杭州国际服务工程学院, 杭州 311121;2.杭州市妇产科医院, 杭州 310008)
Fetal Abnormal Weight Prediction Based on Variable Time Interval LSTM
(1.Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou 311121, China;2.Hangzhou Women's Hospital, Hangzhou 310008, China)
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投稿时间:2019-08-08    修订日期:2019-09-05
中文摘要: 孕妇的产前体检是围产医学的重要组成部分,产前预测胎儿体重可以为判断胎儿健康发育提供准确的参考.孕妇的多次体检记录在孕周时间上有不均匀时间间隔分布的特点.本研究对不均匀时间间隔的处理提出了LSTM模型的变种——变长时间间隔的LSTM模型(Variable Time Interval LSTM,VTI-LSTM).本研究数据来源于2015~2018年多家妇产科医院的10 473个孕妇的122 462条体检记录.实验比较了传统的公式估算法以及GBDT,MLP,SVR,RNN,LSTM,VTI-LSTM等机器学习方法的胎儿体重预测结果,其中,VTI-LSTM在低体重和巨大儿的预测上取得良好的预测结果.
Abstract:Prenatal physical examination of pregnant women is a import part of perinatal medicine. Prenatal prediction of fetal weight can provide an accurate reference for judging the healthy development of the fetus. The multiple physical examination records have the characteristics of variable time interval distribution during gestational period. This study proposes a variant of LSTM model, Variable Time Interval LSTM (VTI-LSTM), to solve the variable time intervals problem. The data of this study were from 122 462 medical records of 10 473 pregnant women from several women's hospitals during 2015 to 2018. The experiments of fetal weight prediction compare the traditional formula estimation methods with the machine learning methods such as GBDT, MLP, SVR, RNN, LSTM, and VTI-LSTM. The results show that Variable Time Interval LSTM has a good prediction result in the prediction of low birth-weight fetal and macrosomia.
文章编号:7334     中图分类号:    文献标志码:
基金项目:国家卫生健康委员会科研项目(WKJ-ZJ-1911);杭州市卫生科技计划一般项目(OO2019054)
引用文本:
张硕彦,吴英飞,袁贞明,卢莎,胡文胜.基于变长时间间隔LSTM方法的胎儿异常体重预测.计算机系统应用,2020,29(3):39-46
ZHANG Shuo-Yan,WU Ying-Fei,YUAN Zhen-Ming,LU Sha,HU Wen-Sheng.Fetal Abnormal Weight Prediction Based on Variable Time Interval LSTM.COMPUTER SYSTEMS APPLICATIONS,2020,29(3):39-46

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