Royal Philips, the Defense Threat Reduction Agency (DTRA), and Defense Innovation Unit (DIU) of the U.S. Department of Defense (DoD) 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.
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.
Unlike other narrow attempts to predict human infection, the RATE approach uses large-scale data machine learning and trade-space analyses across 165 different biomarkers from a rich Philips dataset of over 41,000 cases of HAIs. The dataset was extracted from a large data repository of more than seven million hospital patient encounters. The pared down cases were used as a surrogate dataset for infection in otherwise healthy military personnel and analyzed to develop a predictive algorithm of disease. The performance of the algorithm to predict infection 48 hours before clinical suspicion can be characterized technically as area-under-the-curve of 0.853. For comparison, this performance lies in between blood-based breast and prostate cancer screening tests and an enzyme immunoassay based first-tier Lyme disease test.