1. A medical modeling system for predicting a risk of a physiological condition for a patient, said medical modeling system comprising: at least one processor programmed to: receive measurements of a plurality of predictive variables for the patient, the plurality of predictive variables predictive of the risk of the physiological condition; calculate the risk of the physiological condition by applying the received measurements to at least one model modeling the risk of the physiological condition using the plurality of predictive variables, the calculating including: receiving a time series of measurements for a predictive variable of the plurality of predictive variables; calculating a predicted risk of the physiological condition by applying the received times series of measurements to a hidden Markov model modeling the risk of the physiological condition; and calculating the risk of the physiological condition by applying the received measurements and the calculated predicted risk to a logistic regression model modeling the risk of the physiological condition; output an indication of the risk of the physiological condition to a clinician; train at least one of the hidden Markov model and the logistic regression model based on a training population before calculating the risk of the physiological condition; retrain the at least one of the hidden Markov model and the logistic regression model based on the received measurements and an outcome describing whether the physiological condition occurred in the patient; and calculate the risk of the physiological condition for another patient using the retrained model; a clinical decision system programmed to: receive the calculated risk of the physiological condition; provide a clinician with clinical decision support based on the calculated risk of the physiological condition; monitor the calculated risk of the physiological condition for an alarm condition; and in response to the alarm condition, control an alarm output unit to output an alarm indicative of the physiological condition to the clinician.