基于变长时间间隔LSTM方法的胎儿异常体重预测

Fetal Abnormal Weight Prediction Based on Variable Time Interval LSTM

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10473个孕妇的122462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.

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 122462 medical records of 10473 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.