Pre-fever Detection of Infection

Exposure to agents such as toxins or pathogens can lead to a range of diseases, making timely detection crucial to patient outcomes. Current methods of detection often rely on observable symptoms or lab testing, both of which can be time-consuming and may not detect exposure until symptoms have progressed significantly. Too often, by the time a positive detection of agent exposure has been made, the patient could have already experienced irreversible harm. Methodologies are needed for the prediction of such exposures in a more timely, efficient manner.

Technology Description

This technology comprises systems and methods capable of predicting if a patient has been exposed to an agent. The method works by recording the patient's physiological data during a specific time lapse and exracting from this raw data various features for further analysis. Several classifiers trained on different "post-exposure" time intervals analyze these features to predict a patient's exposure. Each classifier works independently and produces its own prediction, known as a patient state classification. What makes this technology stand out is its method of confirming exposure to the agent. If a certain threshold of these state classifications predict positive exposure, the system concludes the patient has been exposed to the agent. This recognition process is not reliant on a single prediction but on a collective outcome, thus significantly reducing the chances of false predictions.