Wang Lin-Yu, Wang Lin-Yen, Sung Mei-I, Lin I-Chun, Liu Chung-Feng, Chen Chia-Jung
Department of Pediatrics, Chi Mei Medical Center, Tainan City 71004, Taiwan.
Center for General Education, Southern Taiwan University of Science and Technology, Tainan City 71005, Taiwan.
Diagnostics (Basel). 2024 Jul 19;14(14):1571. doi: 10.3390/diagnostics14141571.
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.
低血糖是新生儿期常见的代谢紊乱疾病。早期识别有发生低血糖风险的新生儿可优化新生儿护理中的治疗策略。本研究旨在开发一种机器学习模型并实施一种预测应用程序，以协助临床医生准确预测出生后四小时内新生儿低血糖的风险。我们的回顾性研究分析了2011年1月1日至2021年8月31日期间胎龄≥35周且入住健康婴儿保育室的新生儿数据。我们从台湾南部一家三级医疗中心收集了2687名新生儿的电子病历。利用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。我们选择了受试者操作特征曲线（AUC）下面积最高的模型，将其集成到我们的医院信息系统（HIS）中。早期新生儿低血糖预测模型的前三个AUC值分别为：Stacking为0.739，随机森林为0.732，投票法为0.732。随机森林被认为是最佳模型，因为它具有相对较高的AUC，且未显示出明显的过拟合（准确率为0.658，灵敏度为0.682，特异性为0.649，F1分数为0.517，精确率为0.417）。最佳模型被纳入集成到医院信息系统的基于网络的应用程序中。夏普利值表明分娩方式、胎龄、多胎妊娠、呼吸窘迫和出生体重<2500克是新生儿低血糖的前五大预测因素。我们机器学习模型的实施提供了一种有效工具，可协助临床医生准确识别有早期新生儿低血糖风险的新生儿，从而实现及时干预和治疗。
