From a Health IT Analytics online release:
The prototype revealed that using artificial intelligence and machine learning to examine certain combinations of vital signs and other biomarkers could strongly predict the likelihood of infection up to 48 hours in advance of clinical suspicion, including observable symptoms.
Royal Philips, in collaboration with the Defense Threat Reduction Agency (DTRA) and Defense Innovation Unit (DIU) of the US Department of Defense (DoD), are building a machine learning algorithm that will be able to detect an infection before a patient shows signs or symptoms.
The partnering organizations recently announced results from an 18-month project, called Rapid Analysis of Threat Exposure (RATE), the first large-scale exploration of pre-symptomatic infection in humans. The project aims to develop an early warning system that accelerates diagnosis and treatment of infection, containing the spread of communicable disease.