A method for predicting physiological phenomena, the method comprising: receiving real-time physiological data from a physiological sensor (130, 204-1, 304-2, 304-3) that is measuring a physiological characteristic of a patient; deriving (412, 528) physiological feature data from the physiological data; characterized by determining, from a predictive model, a physiological threshold from the physiological feature data and from historical data, wherein the physiological threshold corresponds to a point such that when the physiological feature data reaches the physiological threshold abnormal physiology is deemed to be present, the predictive model comprising a set of probabilistic predictive model ô k = M k ( S k), where ô k is a prediction of outcome o k derived from the model M k that uses as inputs values obtained from a set of signals S k derived using the physiological data from the physiological sensor (130, 204-1, 304-2, 304-3); and providing at a user interface the relationship between the physiological threshold and the physiological feature data as physiological feature data is derived from the physiological data; wherein the predictive model is determined using the steps of: identifying the set of signals S k as a most-predictive set of signals S k out of a set of signals s 1, s 2,..., s D for each of one or more outcomes o k; autonomously learning the set of probabilistic predictive models ô k = M k ( S k), where ô k is a prediction of outcome o k derived from the model M k that uses as inputs values obtained from the set of signals S k; repeating the step of autonomously learning incrementally from data that contains examples of values of signals s 1; s 2,..., s D and corresponding outcomes o 1, o 2,..., o K; and building the predictive model from the signals and corresponding outcomes. receiving real-time physiological data from a physiological sensor (130, 204-1, 304-2, 304-3) that is measuring a physiological characteristic of a patient; deriving (412, 528) physiological feature data from the physiological data; characterized by determining, from a predictive model, a physiological threshold from the physiological feature data and from historical data, wherein the physiological threshold corresponds to a point such that when the physiological feature data reaches the physiological threshold abnormal physiology is deemed to be present, the predictive model comprising a set of probabilistic predictive model ô k = M k ( S k), where ô k is a prediction of outcome o k derived from the model M k that uses as inputs values obtained from a set of signals S k derived using the physiological data from the physiological sensor (130, 204-1, 304-2, 304-3); and providing at a user interface the relationship between the physiological threshold and the physiological feature data as physiological feature data is derived from the physiological data; wherein the predictive model is determined using the steps of: identifying the set of signals S k as a most-predictive set of signals S k out of a set of signals s 1, s 2,..., s D for each of one or more outcomes o k; autonomously learning the set of probabilistic predictive models ô k = M k ( S k), where ô k is a prediction of outcome o k derived from the model M k that uses as inputs values obtained from the set of signals S k; repeating the step of autonomously learning incrementally from data that contains examples of values of signals s 1; s 2,..., s D and corresponding outcomes o 1, o 2,..., o K; and building the predictive model from the signals and corresponding outcomes.