基于宫缩信号和LightGBM的产时缩宫素剂量预测

Dose Prediction of Oxytocin During Labor Based on Uterine Contraction Signal and LightGBM

缩宫素是产科催产、引产和产前胎儿监测的首选药物,产时缩宫素剂量调控不当可增加不良妊娠结局的风险,目前临床缩宫素的输注主要依靠医护人员手动调节,但在人工调控过程中存在主观判断误差,且人力成本高昂,而现有的缩宫素注射系统也缺少有效的智能调控手段.因此,本文提出一种智能缩宫素剂量预测方法,对胎心监护仪的宫缩信号进行特征提取,结合胎心、心率、电子病历、护理记录等数据,构建基于贝叶斯优化(BOA)的LightGBM缩宫素剂量预测模型.实验选取浙江省某三甲医院采集的10061条样本数据,相较于传统模型,BOALightGBM预测性能更佳.因此,将本文预测模型应用于产科缩宫素用药调节是可行的,可为产科医护人员产时临床决策提供辅助支持,减轻人力成本,实现精准给药,对临床工作具有积极意义.

Oxytocin is the first choice for labor induction,induced abortion,and prenatal fetal monitoring.Improper dose control of oxytocin during labor can increase the risk of adverse pregnancy outcomes.However,clinical oxytocin infusion mainly depends on the manual adjustment of medical staff,leading to subjective judgment errors in doses and high human cost.In addition,the existing oxytocin injection system lacks effective intelligent control means.Therefore,this study proposes to design an intelligent program for oxytocin dose control.It can extract the features of uterine contraction signals of a fetal heart monitor,and combined with fetal heart rate,electronic medical records,nursing records and other data,a prediction model of oxytocin doses was designed based on BOA-LightGBM.The experimental results show that LightGBM optimized by Bayesian is feasible to control oxytocin doses in real time compared with the traditional model.Therefore,this study can provide decision support for obstetric medical staff to adjust oxytocin doses during labor.It plays a positive role in reducing labor costs and enabling accurate drug delivery.