基于Jensen-Shannon散度的ICU患者脓毒症在线临界状态检测
Online Critical-State Detection of Sepsis Among ICU Patients using Jensen-Shannon Divergence
摘要：
脓毒症是由宿主对感染的反应失调引起的严重医学病症，其具有高发病率和死亡率。即使如此高的发生率，脓毒症的检测和诊断仍然是一个挑战。有一个关键的需要，以准确地预测败血症的发病迅速，同时也确定特定的生理异常，有助于这种预测在一个可解释的方式。本研究提出了一种新的方法来定量测量患者和参考组之间的差异，使用非参数概率分布估计和突出异常时出现使用基于詹森-香农分歧的单样本分析方法。我们表明，我们可以定量区分这两组，并提供一个实时的分歧，同时确定特定的生理因素对患者的结果。我们展示了我们的方法在现实世界中的数据集的患者入住亚特兰大，佐治亚州的格雷迪医院。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
