Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the survival of sepsis patients. Existing predictive models are usually trained on high-quality data with few missing information, while missing values widely exist in real-world clinical scenarios (especially in the first hours of admissions to the hospital), which causes a significant decrease in accuracy and an increase in uncertainty for the predictive models. The common method to handle missing values is imputation, which replaces the unavailable variables with estimates from the observed data. The uncertainty of imputation results can be propagated to the sepsis prediction outputs, which have not been studied in existing works on either sepsis prediction or uncertainty quantification. In this study, we first define such propagated uncertainty as the variance of prediction output and then introduce uncertainty propagation methods to quantify the propagated uncertainty. Moreover, for the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm to increase confidence by actively recommending clinicians to observe the most informative variables. We validate the proposed models in both publicly available data (i.e., MIMIC-III and AmsterdamUMCdb) and proprietary data in The Ohio State University Wexner Medical Center (OSUWMC). The experimental results show that the propagated uncertainty is dominant at the beginning of admissions to hospitals and the proposed algorithm outperforms state-of-the-art active sensing methods. Finally, we implement a SepsisLab system for early sepsis prediction and active sensing based on our pre-trained models. Clinicians and potential sepsis patients can benefit from the system in early prediction and diagnosis of sepsis.

翻译：脓毒症是美国院内死亡的首要原因。早期预测与诊断脓毒症发病可显著提高患者生存率。现有预测模型通常基于缺失信息极少的高质量数据进行训练，而真实临床场景（尤其是入院初期）普遍存在数据缺失，这导致预测模型的准确性显著下降且不确定性增加。处理缺失值的常用方法是插补，即用观测数据的估计值替代不可用变量。插补结果的不确定性会传递至脓毒症预测输出，而现有脓毒症预测或不确定性量化研究均未探讨该问题。本研究首先将此类传递不确定性定义为预测输出的方差，进而引入不确定性传递方法对其进行量化。此外，针对因观测数据有限而置信度较低的潜在高危患者，我们提出一种鲁棒的主动感知算法，通过主动建议临床医生观测信息量最大的变量来提升置信度。我们在公开数据集（即MIMIC-III与AmsterdamUMCdb）及俄亥俄州立大学韦克斯纳医学中心（OSUWMC）的专有数据中验证了所提模型。实验结果表明：传递不确定性在入院初期占主导地位，且所提算法性能优于当前最先进的主动感知方法。最后，我们基于预训练模型实现了用于脓毒症早期预测与主动感知的SepsisLab系统。临床医生与潜在脓毒症患者可借助该系统实现脓毒症的早期预测与诊断。