  Leveraging machine learning techniques for Sepsis early detection and
diagnosis has attracted increasing interest in recent years. However, most
existing methods require a large amount of labeled training data, which may not
be available for a target hospital that deploys a new Sepsis detection system.
More seriously, as treated patients are diversified between hospitals, directly
applying a model trained on other hospitals may not achieve good performance
for the target hospital. To address this issue, we propose a novel
semi-supervised transfer learning framework based on optimal transport theory
and self-paced ensemble for Sepsis early detection, called SPSSOT, which can
efficiently transfer knowledge from the source hospital (with rich labeled
data) to the target hospital (with scarce labeled data). Specifically, SPSSOT
incorporates a new optimal transport-based semi-supervised domain adaptation
component that can effectively exploit all the unlabeled data in the target
hospital. Moreover, self-paced ensemble is adapted in SPSSOT to alleviate the
class imbalance issue during transfer learning. In a nutshell, SPSSOT is an
end-to-end transfer learning method that automatically selects suitable samples
from two domains (hospitals) respectively and aligns their feature spaces.
Extensive experiments on two open clinical datasets, MIMIC-III and Challenge,
demonstrate that SPSSOT outperforms state-of-the-art transfer learning methods
by improving 1-3% of AUC.
