Abstract：
Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.
摘要：
细菌感染是全世界新生儿发病和死亡的主要原因之一。寻找用于细菌感染的早期识别和诊断以及抗菌药物施用的早期个体化的快速和可靠的方法对于根除这些感染和预防严重的并发症至关重要。然而,由于非特异性临床表现，这通常很难执行，当前诊断方法的准确性低，和新生儿药代动力学知识有限。尽管新生儿医学接受机器学习（ML）的好处相对较晚，已经有一些初步应用ML早期预测新生儿败血症和抗生素个体化。本文简要介绍了ML，并讨论了诊断和治疗新生儿细菌感染的最新技术，间隙,ML的潜在用途，以及解决当前研究局限性的未来方向。新生儿细菌感染涉及生理发育的组合，疾病表达，和治疗反应结果。为了解决这种复杂的关系，未来的模型可以考虑适当的ML算法来捕获时间序列特征，同时整合来自主机的影响，微生物,以及优化新生儿抗菌药物使用的药物。所有模型在临床使用前都需要前瞻性临床试验来验证其临床实用性。
