A method includes receiving contextual data related to at least one of environmental, physiological, behavioral, and historical context, and receiving outcome data related to at least one outcome. The method further includes 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 the probability that the feature is a predictor of the at least one outcome, and generating a characteristic curve for the at least one outcome from the subset of features, the characteristic curve being based on the scoring. The method further includes calculating the area under the characteristic curve, and using, the area under the characteristic curve, identifying whether the subset of features is a suitable predictor for the at least one outcome.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: 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 the 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 scoring; calculating an area under the characteristic curve; and identifying, using the area under the characteristic curve, whether the subset of features is a suitable predictor for the at least one outcome. 2. The method of claim 1, wherein generating a 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. 3. The method of claim 1, wherein the characteristic curve is one of a plurality of characteristic curves, further comprising comparing the area under the characteristic curve to areas under other characteristic curves in the plurality of characteristic curves and identifying a preferred predictor based on the comparing. 4. The method of claim 1, wherein the characteristic curve is one of a plurality of characteristic curves, further comprising comparing the area under the characteristic curve to areas under other characteristic curves in the plurality of characteristic curves and identifying a preferred scoring technique based on the comparing. 5. The method of claim 1, further comprising generating a prediction model for the at least one outcome based on the subset of features. 6. The method of claim 5, further comprising: receiving additional contextual data; and predicting an outcome based on the additional contextual data using the prediction model. 7. The method of claim 6, wherein the additional contextual data includes data regarding a group of individuals, and the predicted outcome is a prediction of the outcome for the group of individuals. 8. The method of claim 1, wherein the contextual data includes data from a plurality of sources. 9. The method of claim 1, wherein the contextual data includes data relating to a plurality of individuals. 10. The method of claim 1, wherein the contextual data includes data taken at a plurality of times. 11. A system, comprising: a memory including processor-executable instructions; and a processor configured to execute instructions from the memory; wherein the instructions include instructions for the processor to: receive contextual data and outcome data; create a feature set from the contextual data; select a plurality of feature subsets from the feature set; for each of the plurality of feature subsets: apply the feature subset and the outcome data to a classifier, and determine a score for the feature subset; select a preferred feature subset based on the score for each of the plurality of feature subsets; and generate a prediction model using the preferred feature subset. 12. The system of claim 11, wherein the instructions further include instructions for the processor to cluster the contextual data, and create the feature set from at least a portion of the clustered contextual data. 13. The system of claim 12, wherein the clustered contextual data includes multiple data clusters, and wherein the instructions to create the feature set include instructions to create a plurality of features sets from respective ones of the multiple data clusters. 14. The system of claim 11, wherein the outcome data includes data representing a plurality of different outcomes. 15. The system of claim 11, wherein the instructions further include instructions for the processor to receive additional contextual data, and predict an outcome from the additional contextual data using the prediction model. 16. The system of claim 11, wherein the outcome data represents at least one outcome, and wherein the instructions to determine the score for each of the plurality of feature subsets includes instructions for the processor to, for each feature subset: use the classifier to determine a probability measure for each feature in the feature set, the probability measure being an indication of how predictive the feature is for an outcome of the at least one outcome; create a characteristic curve of true positive rate versus false positive rate, wherein each point of the characteristic curve represents a portion of the feature set, and each portion of the feature set is selected based on a probability measure threshold; and calculate the score as an area under the characteristic curve. 17. A processor-readable physical medium including processor-executable instructions, the instructions comprising instructions for: receiving outcome information and contextual information related to the outcome information; pre-processing the outcome information and contextual information; creating a feature set from the pre-processed contextual information; assigning scores to at least some of the features in the feature set, wherein a score is assigned to a feature according to the ability of the feature to predict an outcome described by the outcome information; determining a set of characteristic curves using the scores, wherein each characteristic curve represents an ability to predict the outcome; calculating a score for each of the characteristic curves in the set of characteristic curves; and based on the score for each of the characteristic curves, identifying a predictor including features from the feature set. 18. The processor-readable physical medium of claim 17, wherein the outcome information includes data representing a plurality of different outcomes. 19. The processor-readable physical medium of claim 17, wherein the pre-processing includes removing redundant features and irrelevant features. 20. The processor-readable physical medium of claim 17, the instructions further comprising instructions for: identify a pool of contextual information related to features included in the predictor; apply the pool to a plurality of scorers individually; determine a characteristic curve for each scorer of the plurality of scorers based on the pool; and select a scorer from the plurality of scorers based on a measurement related to the characteristic curve for the selected scorer. 21. The processor-readable physical medium of claim 17, the instructions for calculating a score including instructions for minimizing a cost of one of data collection and data processing.