1. A method of context-aware prediction of medical conditions, comprising: by a first computing device executing instructions from a non-transitory computer-readable medium for: receiving contextual data related to at least one of environmental, physiological, behavioral, and historical context; receiving outcome data related to at least one outcome; creating a feature set from the contextual data; selecting a subset of features from the feature set; assigning a score to each feature in the subset of features according to a probability that the feature is a predictor of the at least one outcome; generating a characteristic curve for the at least one outcome from the subset of features, the characteristic curve based on the scores of the features in the subset of features; calculating an area under the characteristic curve; identifying, using the area under the characteristic curve, whether the subset of features is a suitable predictor for the at least one outcome; responsive to identifying that the subset of features is a suitable predictor for the at least one outcome, generating a prediction model relating the subset of features to the at least one outcome; and providing to a display device for display a prediction of a likelihood of occurrence of the at least one outcome by applying the prediction model to data received from a monitoring device, wherein generating the characteristic curve includes iteratively: setting a probability threshold; selecting a feature group from the subset of features, wherein, for each feature in the feature group, the assigned score is greater than the probability threshold; determining for the contextual data of features in the feature group a true positive rate and a false positive rate of prediction of the outcome; and plotting the true positive rate and the false positive rate for the probability threshold.