Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals

TL;DR

This paper introduces SofaNet, a privacy-preserving collaborative learning framework guided by medical knowledge, to improve early sepsis detection across hospitals with limited data, leveraging multi-center EMR data and knowledge sharing.

Contribution

SofaNet is a novel multi-channel GRU framework that incorporates medical knowledge and feature distribution alignment for secure cross-center sepsis recognition.

Findings

Improves early sepsis detection accuracy in data-scarce hospitals.

Enhances health status representations through knowledge-guided multi-task learning.

Maintains data privacy by aligning features in hidden space without raw data exchange.

Abstract

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.