Supplementary Material for: Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review

Introduction: Approximately 7,000 newborns die every day, accounting for almost half of child deaths under 5 years of age. Deciphering which neonates are at increased risk for mortality can have an important global impact. As such, integrating high computational technology (e.g., artificial intelligence [AI]) may help identify the early and potentially modifiable predictors of neonatal mortality. Therefore, the objective of this study was to collate, critically appraise, and analyze neonatal prediction studies that included AI. Methods: A literature search was performed in PubMed, Cochrane, OVID, and Google Scholar. We included studies that used AI (e.g., machine learning (ML) and deep learning) to formulate prediction models for neonatal death. We excluded small studies (n < 500 individuals) and studies using only antenatal factors to predict mortality. Two independent investigators screened all articles for inclusion. The data collection consisted of study design, number of models, features used per model, feature importance, internal and/or external validation, and calibration analysis. Our primary outcome was the average area under the receiving characteristic curve (AUC) or sensitivity and specificity for all models included in each study. Results: Of 434 articles, 11 studies were included. The total number of participants was 1.26 M with gestational ages ranging from 22 weeks to term. Number of features ranged from 3 to 66 with timing of prediction as early as 5 min of life to a maximum of 7 days of age. The average number of models per study was 4, with neural network, random forest, and logistic regression comprising the most used models (58.3%). Five studies (45.5%) reported calibration plots and 2 (18.2%) conducted external validation. Eight studies reported results by AUC and 5 studies reported the sensitivity and specificity. The AUC varied from 58.3% to 97.0%. The mean sensitivities ranged from 63% to 80% and specificities from 78% to 99%. The best overall model was linear discriminant analysis, but it also had a high number of features (n = 17). Discussion/Conclusion: ML models can accurately predict death in neonates. This analysis demonstrates the most commonly used predictors and metrics for AI prediction models for neonatal mortality. Future studies should focus on external validation, calibration, as well as deployment of applications that can be readily accessible to health-care providers.

引言：每日约有7000名新生儿去世，占5岁以下儿童死亡人数的一半左右。解读哪些新生儿死亡风险增加，对于全球范围内具有重要影响。因此，整合高级计算技术（例如，人工智能[AI]）有助于识别新生儿死亡早期且可能可更改的预测指标。因此，本研究旨在搜集、批判性地评估和分析包括人工智能在内的新生儿预测研究。方法：在PubMed、Cochrane、OVID和Google Scholar数据库中进行文献检索。我们纳入使用人工智能（例如，机器学习[ML]和深度学习）制定新生儿死亡预测模型的研究。我们排除了样本量较小的研究（n < 500人）以及仅使用产前因素预测死亡的研究。两名独立的调查员对所有文章进行了筛选。数据收集包括研究设计、模型数量、每个模型使用的特征、特征重要性、内部及/或外部验证以及校准分析。我们的主要结局是每个研究中包括的所有模型的平均接收者操作特征曲线下面积（AUC）或灵敏度和特异性。结果：在434篇文章中，有11项研究被纳入。总参与人数为1.26百万，孕周范围从22周到足月。特征数量从3个到66个不等，预测时间从出生后的5分钟到7天。每项研究的平均模型数量为4，其中神经网络、随机森林和逻辑回归是最常用的模型（占58.3%）。五项研究（45.5%）报告了校准图，两项（18.2%）进行了外部验证。八项研究通过AUC报告结果，五项研究报告了灵敏度和特异性。AUC的范围从58.3%到97.0%。平均灵敏度从63%到80%，特异性从78%到99%。最佳整体模型为线性判别分析，但它也具有大量的特征（n = 17）。讨论/结论：机器学习模型可以准确预测新生儿死亡。本分析展示了人工智能预测新生儿死亡模型中最常用的预测指标和度量。未来的研究应聚焦于外部验证、校准，以及应用部署，以便能够迅速提供给医疗保健提供者。