重症监护病房的关键转变：脓毒症病例研究
Critical Transitions in Intensive Care Units: A Sepsis Case Study
摘要：
复杂的人类疾病的进展与跨动态机制的关键转变有关。这些转变通常会产生早期预警信号，并提供对潜在疾病驱动机制的见解。在本文中，我们提出了一种基于意外损失（SL）的计算方法，以发现在多变量时间序列数据集的脓毒性休克和非脓毒症患者队列（MIMIC-III数据库）的这种转变的数据驱动的指标。SL的核心思想是以无监督的方式在时间序列上训练一个数学模型，并量化模型预测（样本外）性能相对于其过去（样本内）性能的恶化。考虑到SL移动平均值的最高值作为关键转变，我们的回顾性分析显示，关键转变发生在感染性休克发作前35小时的中位数，这表明我们的方法作为预警指标的适用性。此外，我们发现，在临界过渡区的临床变量是显着不同的感染性休克和非败血症队列。因此，我们的论文提供了一个基于关键转换的数据采样策略，可用于进一步的分析，如患者分类。此外，我们的方法优于其他指标的临界过渡的复杂系统，如时间自相关和方差。
英文摘要：
The progression of complex human diseases is associated with critical transitions across dynamical regimes. These transitions often spawn early-warning signals and provide insights into the underlying disease-driving mechanisms. In this paper, we propose a computational method based on surprise loss (SL) to discover data-driven indicators of such transitions in a multivariate time series dataset of septic shock and non-sepsis patient cohorts (MIMIC-III database). The core idea of SL is to train a mathematical model on time series in an unsupervised fashion and to quantify the deterioration of the model's forecast (out-of-sample) performance relative to its past (in-sample) performance. Considering the highest value of the moving average of SL as a critical transition, our retrospective analysis revealed that critical transitions occurred at a median of over 35 hours before the onset of septic shock, which suggests the applicability of our method as an early-warning indicator. Furthermore, we show that clinical variables at critical-transition regions are significantly different between septic shock and non-sepsis cohorts. Therefore, our paper contributes a critical-transition-based data-sampling strategy that can be utilized for further analysis, such as patient classification. Moreover, our method outperformed other indicators of critical transition in complex systems, such as temporal autocorrelation and variance.
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。
