The utilization of computer technology to solve problems in medical scenarios
has attracted considerable attention in recent years, which still has great
potential and space for exploration. Among them, machine learning has been
widely used in the prediction, diagnosis and even treatment of Sepsis. However,
state-of-the-art methods require large amounts of labeled medical data for
supervised learning. In real-world applications, the lack of labeled data will
cause enormous obstacles if one hospital wants to deploy a new Sepsis detection
system. Different from the supervised learning setting, we need to use known
information (e.g., from another hospital with rich labeled data) to help build
a model with acceptable performance, i.e., transfer learning. In this paper, we
propose a semi-supervised optimal transport with self-paced ensemble framework
for Sepsis early detection, called SPSSOT, to transfer knowledge from the other
that has rich labeled data. In SPSSOT, we first extract the same clinical
indicators from the source domain (e.g., hospital with rich labeled data) and
the target domain (e.g., hospital with little labeled data), then we combine
the semi-supervised domain adaptation based on optimal transport theory with
self-paced under-sampling to avoid a negative transfer possibly caused by
covariate shift and class imbalance. On the whole, SPSSOT is an end-to-end
transfer learning method for Sepsis early detection which can automatically
select suitable samples from two domains respectively according to the number
of iterations and align feature space of two domains. Extensive experiments on
two open clinical datasets demonstrate that comparing with other methods, our
proposed SPSSOT, can significantly improve the AUC values with only 1% labeled
data in the target domain in two transfer learning scenarios, MIMIC
rightarrow Challenge and Challenge rightarrow MIMIC.Comment: 14 pages, 9 figure