PDF(Pubmed)
Abstract：
Hypoglycemia is a common metabolic disorder that occurs in the neonatal period. Early identification of neonates at risk of developing hypoglycemia can optimize therapeutic strategies in neonatal care. This study aims to develop a machine learning model and implement a predictive application to assist clinicians in accurately predicting the risk of neonatal hypoglycemia within four hours after birth. Our retrospective study analyzed data from neonates born ≥35 weeks gestational age and admitted to the well-baby nursery between 1 January 2011 and 31 August 2021. We collected electronic medical records of 2687 neonates from a tertiary medical center in Southern Taiwan. Using 12 clinically relevant features, we evaluated nine machine learning approaches to build the predictive models. We selected the models with the highest area under the receiver operating characteristic curve (AUC) for integration into our hospital information system (HIS). The top three AUC values for the early neonatal hypoglycemia prediction models were 0.739 for Stacking, 0.732 for Random Forest and 0.732 for Voting. Random Forest is considered the best model because it has a relatively high AUC and shows no significant overfitting (accuracy of 0.658, sensitivity of 0.682, specificity of 0.649, F1 score of 0.517 and precision of 0.417). The best model was incorporated in the web-based application integrated into the hospital information system. Shapley Additive Explanation (SHAP) values indicated mode of delivery, gestational age, multiparity, respiratory distress, and birth weight < 2500 gm as the top five predictors of neonatal hypoglycemia. The implementation of our machine learning model provides an effective tool that assists clinicians in accurately identifying at-risk neonates for early neonatal hypoglycemia, thereby allowing timely interventions and treatments.
摘要：
低血糖是一种常见的代谢紊乱，发生在新生儿期。早期识别有低血糖风险的新生儿可以优化新生儿护理的治疗策略。这项研究旨在开发机器学习模型并实施预测性应用程序，以协助临床医生在出生后四小时内准确预测新生儿低血糖的风险。我们的回顾性研究分析了在2011年1月1日至2021年8月31日期间出生≥35周胎龄并进入婴儿托儿所的新生儿的数据。我们从台湾南部的三级医疗中心收集了2687名新生儿的电子病历。使用12个临床相关特征，我们评估了九种机器学习方法来构建预测模型。我们选择了接收器工作特征曲线（AUC）下面积最大的模型，以集成到我们的医院信息系统（HIS）中。Stacking早期新生儿低血糖预测模型的前3个AUC值分别为0.739,随机森林为0.732，投票为0.732。随机森林被认为是最好的模型，因为它具有相对较高的AUC，并且没有显示出明显的过拟合（精度为0.658，灵敏度为0.682，特异性为0.649，F1评分为0.517，精度为0.417）。最佳模型已集成到基于Web的应用程序中，该应用程序集成到医院信息系统中。Shapley添加剂解释(SHAP)值表示输送模式，胎龄,多重奇偶校验，呼吸窘迫,出生体重<2500gm是新生儿低血糖的五大预测因子。我们的机器学习模型的实施提供了一种有效的工具，可以帮助临床医生准确识别早期新生儿低血糖的风险新生儿，从而允许及时的干预和治疗。
