基于LSTM与XGBOOST混合模型的孕妇产后出血预测

孕妇产后大出血是造成全球孕妇死亡的重要因素之一，在我国位居孕妇死亡原因首位，然而对产后出血的提前判定一直以来都是医学上一个难题.电子病历的普及，以及机器学习和深度学习技术的发展，为预知孕妇产后大出血提供了基于大数据的解决办法.本文提出利用孕妇的电子病历数据，构建基于LSTM和XGBoost的混合模型来预测孕妇产后大出血.实验结果表明，利用基于LSTM和XGBoost的混合模型对孕妇产后大出血进行预测是可行的，能够为医生判断孕妇产后出血情况提供参考，为孕妇分娩时是否需要备血方案提供决策支持，对降低产后大出血致死率具有积极意义.

Predictive Analysis of Postpartum Hemorrhage Based on LSTM and XGBoost Hybrid Model

Postpartum hemorrhage in pregnant women is one of the most important factors of maternal death around the world, ranking first in China. However, the early diagnosis of postpartum hemorrhage has always been a medical problem. With the popularity of Electronic Health Records and the development of machine learning and deep learning technologies, new solutions have been provided for predicting postpartum hemorrhage in pregnant women. This study proposes to construct a mixed prediction model of postpartum hemorrhage based on LSTM and XGBoost by using the Electronic Health Records of pregnant women. The experimental results show that the hybrid model based on LSTM and XGBoost is feasible to predict postpartum hemorrhage in pregnant women. It can provide a reference for doctors to judge the situation of postpartum hemorrhage and provide decision-making support for whether blood preparation would be needed during delivery. It is of positive significance to reduce the mortality rate of postpartum hemorrhage.