Espinola-Sánchez Marcos, Sanca-Valeriano Silvia, Campaña-Acuña Andres, Caballero-Alvarado José
Facultad de Ciencias de la Salud, Universidad Privada del Norte, Peru.
Centro de Innovación e Investigación Traslacional en Salud, Universidad Privada del Norte, Peru.
Heliyon. 2023 Oct 5;9(10):e20693. doi: 10.1016/j.heliyon.2023.e20693. eCollection 2023 Oct.
Neonatal mortality remains a critical concern, particularly in developing countries. The advent of machine learning offers a promising avenue for predicting the survival of at-risk neonates. Further research is required to effectively deploy this approach within distinct clinical contexts. : This study aimed to assess the applicability of machine learning models in predicting neonatal mortality, drawing from maternal and clinical characteristics of pregnant women within an intensive care unit (ICU).
Conducted as an observational cross-sectional study, the research enrolled pregnant women receiving care in a level III national hospital's ICU in Peru. Detailed data encompassing maternal diagnosis, maternal characteristics, obstetric characteristics, and newborn outcomes (survival or demise) were meticulously collected. Employing machine learning, predictive models were developed for neonatal mortality. Estimations of beta coefficients in the training dataset informed the model application to the validation dataset.
A cohort of 280 pregnant women in the ICU were included in this study. The Gradient Boosting approach was selected following rigorous experimentation with diverse model types due to its superior F1-score, ROC curve performance, computational efficiency, and learning rate. The final model incorporated variables deemed pertinent to its efficacy, including gestational age, eclampsia, kidney infection, maternal age, previous placenta complications accompanied by hemorrhage, severe preeclampsia, number of prenatal checkups, and history of miscarriages. By incorporating optimized hyperparameter values, the model exhibited an impressive area under the curve (AUC) of 0.98 (95 % CI: 0.95-1), along with a sensitivity of 0.98 (95 % CI: 0.94-1) and specificity of 0.98 (95 % CI: 0.93-1).
The findings underscore the utility of machine learning models, specifically Gradient Boosting, in foreseeing neonatal mortality among pregnant women admitted to the ICU, even when confronted with maternal morbidities. This insight can enhance clinical decision-making and ultimately reduce neonatal mortality rates.
新生儿死亡率仍然是一个关键问题，尤其是在发展中国家。机器学习的出现为预测高危新生儿的生存情况提供了一条有前景的途径。需要进一步研究以在不同的临床环境中有效应用这种方法。本研究旨在通过重症监护病房（ICU）内孕妇的母体和临床特征，评估机器学习模型在预测新生儿死亡率方面的适用性。
该研究作为一项观察性横断面研究，纳入了在秘鲁一家三级国立医院ICU接受治疗的孕妇。精心收集了包括母体诊断、母体特征、产科特征和新生儿结局（存活或死亡）的详细数据。利用机器学习开发了新生儿死亡率预测模型。训练数据集中的β系数估计为验证数据集的模型应用提供了依据。
本研究纳入了ICU中的280名孕妇队列。经过对多种模型类型的严格试验，由于其优越的F1分数、ROC曲线性能、计算效率和学习率，选择了梯度提升方法。最终模型纳入了被认为与其疗效相关的变量，包括孕周、子痫、肾脏感染、产妇年龄、既往伴有出血的胎盘并发症、重度子痫前期、产前检查次数和流产史。通过纳入优化的超参数值，该模型的曲线下面积（AUC）达到了令人印象深刻的0.98（95%CI：0.95 - 1），敏感性为0.98（95%CI：0.94 - 1），特异性为0.98（95%CI：0.93 - 1）。
研究结果强调了机器学习模型，特别是梯度提升模型，在预测入住ICU的孕妇新生儿死亡率方面的实用性，即使面对母体疾病也是如此。这一见解可以加强临床决策，最终降低新生儿死亡率。
