Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a novel deep learning model, DeepPBSMonitor (Deep Preterm Birth Survival Risk Monitor), to predict the mortality risk of preterm infants during initial NICU hospitalization. The proposed deep learning model can effectively integrate time-series vital sign data and fixed variables while resolving the influence of noise and imbalanced data. The proposed model was evaluated and compared with other approaches using data from 285 infants. Results showed that the DeepPBSMonitor model outperforms other approaches, with an accuracy, recall, and AUC score of 0.888, 0.780, and 0.897, respectively. In conclusion, the proposed model has demonstrated efficacy in predicting the real-time mortality risk of preterm infants in initial NICU hospitalization.

死亡率仍然是极早产的特殊负担。当前的临床死亡率预测分数是使用一些静态变量测量值来计算的，例如胎龄、出生体重、体温和入院时的血压。虽然这些模型确实提供了一些洞察力，但数字和时间序列生命体征数据也可用于入住 NICU 的早产儿，并且可以更深入地了解结果。通过实时整合生命体征数据和静态临床变量来预测 NICU 早产死亡风险的计算模型可能在临床上有帮助，并且可能优于静态预测模型。但是，缺乏针对此特定任务的既定计算模型。在这项研究中，我们开发了一种新颖的深度学习模型，DeepPBSMonitor（深度早产生存风险监测器），用于预测新生儿重症监护病房初始住院期间早产儿的死亡风险。所提出的深度学习模型可以有效地整合时间序列生命体征数据和固定变量，同时解决噪声和不平衡数据的影响。使用来自 285 名婴儿的数据对提议的模型进行了评估并与其他方法进行了比较。结果表明，DeepPBSMonitor模型优于其他方法，准确率、召回率和 AUC 分数分别为 0.888、0.780 和 0.897。总之，所提出的模型已证明在预测新生儿重症监护病房初始住院期间早产儿的实时死亡风险方面是有效的。