  There are significant regional inequities in health resources around the
world. It has become one of the most focused topics to improve health services
for data-scarce hospitals and promote health equity through knowledge sharing
among medical institutions. Because electronic medical records (EMRs) contain
sensitive personal information, privacy protection is unavoidable and essential
for multi-hospital collaboration. In this paper, for a common disease in ICU
patients, sepsis, we propose a novel cross-center collaborative learning
framework guided by medical knowledge, SofaNet, to achieve early recognition of
this disease. The Sepsis-3 guideline, published in 2016, defines that sepsis
can be diagnosed by satisfying both suspicion of infection and Sequential Organ
Failure Assessment (SOFA) greater than or equal to 2. Based on this knowledge,
SofaNet adopts a multi-channel GRU structure to predict SOFA values of
different systems, which can be seen as an auxiliary task to generate better
health status representations for sepsis recognition. Moreover, we only achieve
feature distribution alignment in the hidden space during cross-center
collaborative learning, which ensures secure and compliant knowledge transfer
without raw data exchange. Extensive experiments on two open clinical datasets,
MIMIC-III and Challenge, demonstrate that SofaNet can benefit early sepsis
recognition when hospitals only have limited EMRs.
