Sepsis is a life-threatening organ malfunction caused by the host's inability to fight infection, which can lead to death without proper and immediate treatment. Therefore, early diagnosis and medical treatment of sepsis in critically ill populations at high risk for sepsis and sepsis-associated mortality are vital to providing the patient with rapid therapy. Studies show that advancing sepsis detection by 6 hours leads to earlier administration of antibiotics, which is associated with improved mortality. However, clinical scores like Sequential Organ Failure Assessment (SOFA) are not applicable for early prediction, while machine learning algorithms can help capture the progressing pattern for early prediction. Therefore, we aim to develop a machine learning algorithm that predicts sepsis onset 6 hours before it is suspected clinically. Although some machine learning algorithms have been applied to sepsis prediction, many of them did not consider the fact that six hours is not a small gap. To overcome this big gap challenge, we explore a multi-subset approach in which the likelihood of sepsis occurring earlier than 6 hours is output from a previous subset and feed to the target subset as additional features. Moreover, we use the hourly sampled data like vital signs in an observation window to derive a temporal change trend to further assist, which however is often ignored by previous studies. Our empirical study shows that both the multi-subset approach to alleviating the 6-hour gap and the added temporal trend features can help improve the performance of sepsis-related early prediction.

翻译：脓毒症是由宿主无法抵抗感染导致的危及生命的器官功能障碍，若不及时正确治疗可能致命。因此，对脓毒症高危人群及脓毒症相关死亡风险较高的危重患者进行早期诊断和医疗干预，对于为患者提供快速治疗至关重要。研究表明，将脓毒症检测提前6小时可更早使用抗生素，这与死亡率改善相关。然而，诸如序贯器官衰竭评估（SOFA）等临床评分不适用于早期预测，而机器学习算法有助于捕捉进展模式以实现早期预测。因此，我们旨在开发一种机器学习算法，在临床怀疑脓毒症之前6小时预测其发生。尽管已有部分机器学习算法应用于脓毒症预测，但许多算法并未考虑6小时并非短时间间隔这一事实。为克服这一较长间隔的挑战，我们探索了一种多子集方法，其中将早于6小时发生脓毒症的可能性从前一子集输出，并作为附加特征输入目标子集。此外，我们利用观察窗口内每小时采样的生命体征等数据推导时间变化趋势以进一步辅助预测，而这一点常被以往研究忽视。实证研究表明，缓解6小时间隔的多子集方法以及添加的时间趋势特征均有助于提升脓毒症早期预测的性能。