ANDOVER, Mass. & WASHINGTON--(BUSINESS WIRE)--Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, the Defense Threat Reduction Agency (DTRA), and Defense Innovation Unit (DIU) of the U.S. Department of Defense (DoD) today announced highlights from an 18-month project in predictive health monitoring aimed at developing an early warning algorithm to detect infection before an individual shows signs or symptoms. The project, Rapid Analysis of Threat Exposure (RATE), is the first large-scale empirical exploration of prediction of pre-symptomatic infection in humans and is part of efforts to improve readiness, as well as being broadly applicable in healthcare settings. As envisioned by DTRA, an early warning system that facilitates faster diagnosis and treatment of infection can reduce individual downtime and aid in quickly containing the spread of a communicable disease by isolating exposed individuals sooner.

The prototype revealed that using artificial intelligence (AI) to look at 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. In addition, it found that the combinations of significant vital signs and biomarkers varied based on time before clinical suspicion of a hospital acquired infection (HAI). Future research is currently being planned to leverage this information as an algorithm to be integrated into a wearable device, allowing a soldier’s health to be non-invasively monitored and delivering earlier alerts to potential infection. The technology could further be applied in a civilian capacity by helping to monitor hospital patients for infection prior to clinical symptoms.

“The unique capability that Philips has produced enables the chemical and biological defense medical paradigm to shift from a reactionary focused one to a predictive one. This provides our commanders with insight into their troops’ future readiness levels and can influence mission planning and overall military effectiveness,” said Edward Argenta, Science and Technology Manager for the Joint Science & Technology Office at DTRA.
Traditional approaches to diagnosing infections rely on recognition of overt signs, which can mean implementing medical countermeasures after active duty personnel have already been compromised and potentially exposed others. Characterizing pre-symptomatic sentinels indicative of infection using AI mechanisms can help reduce time to diagnosis and treatment, but as with any AI, this process requires a large reliable dataset.

Artificial Intelligence Homepage