Georgia Institute of Technology, Atlanta, Georgia, USA.
Emory University School of Medicine Atlanta, Georgia, USA.
AMIA Annu Symp Proc. 2023 Apr 29;2022:982-991. eCollection 2022.
Sepsis is a severe medical condition caused by a dysregulated host response to infection that has a high incidence and mortality rate. Even with such a high-level occurrence rate, the detection and diagnosis of sepsis continues to pose a challenge. There is a crucial need to accurately forecast the onset of sepsis promptly while also identifying the specific physiologic anomalies that contribute to this prediction in an interpretable fashion. This study proposes a novel approach to quantitatively measure the difference between patients and a reference group using non-parametric probability distribution estimates and highlight when abnormalities emerge using a Jensen-Shannon divergence- based single sample analysis approach. We show that we can quantitatively distinguish between these two groups and offer a measurement of divergence in real time while simultaneously identifying specific physiologic factors contributing to patient outcomes. We demonstrate our approach on a real-world dataset of patients admitted to Atlanta, Georgia's Grady Hospital.
脓毒症是一种严重的医学病症，由宿主对感染的失调反应引起，具有较高的发病率和死亡率。即使脓毒症的发生率如此之高，脓毒症的检测和诊断仍然是一个挑战。迫切需要准确地预测脓毒症的发作，同时以可解释的方式识别导致这种预测的特定生理异常。本研究提出了一种新的方法，使用非参数概率分布估计来定量测量患者与参考组之间的差异，并使用基于 Jensen-Shannon 散度的单样本分析方法来突出异常何时出现。我们表明，我们可以定量区分这两组，并实时提供散度的测量，同时识别出导致患者结果的特定生理因素。我们在亚特兰大格鲁迪医院（Atlanta, Georgia's Grady Hospital）的一个真实患者数据集上展示了我们的方法。
