Clinical ground truth in machine learning for early sepsis diagnosis

This is the authors' abstract. We don't add key points for this paper.

Sepsis is a leading cause of global and hospital mortality without a gold standard diagnostic test available to date. Initial haemodynamic stabilisation and the timely administration of antibiotics improve a patient's outcome,1 but the need for tools supporting the time-sensitive clinical sepsis diagnosis persists. The application of machine learning to electronic medical records (EMRs) as model training data is gaining popularity for the development of individual-level surveillance algorithms for sepsis in intensive care units, hospital wards, and emergency departments.