Abstract: Systems and methods are disclosed herein for predicting whether a patient has been exposed to an agent. Physiological data is recorded from the patient during a first time interval, and one or more features are extracted from the physiological data. A plurality of classifiers is identified, wherein each classifier is trained using training data for a respective specific post-exposure time interval. For each classifier and based on a respective subset of the one or more features, a patient state classification that indicates an initial prediction of whether the patient has been exposed to the agent is determined. An indication of a prediction that the patient has been exposed to the agent is provided when a number of patient state classifications indicating a positive initial prediction that the patient has been exposed to the agent exceeds a first threshold.

Abstract: Systems and methods for predicting exposure to an agent. One or more features are extracted from physiological data. For each respective classifier, (i) the respective classifier is identified, wherein the respective classifier is trained using training data for a respective physiological state, (ii) the respective classifier is applied to the one or more features to obtain a classifier output that represents a likelihood of exposure, (iii) a respective first threshold is applied to the classifier output to determine a patient state classification, and (iv) the patient state classifications are aggregated across a number of time intervals to obtain an aggregate patient state classification for each classifier. The aggregate patient state classifications are combined across the plurality of classifiers to obtain a combined classification, and an indication that the patient has been exposed to the agent is provided when the combined classification exceeds a second threshold.