  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.
