Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, can be implemented to perform actions including receiving input data defining a predictive model, the predictive model including multiple features. The actions further include weighting the predictive model iteratively for each feature, using actual data including values for each feature for multiple entities within a population, by iteratively adjusting a current weight for the feature by a momentum until the momentum equals zero, the momentum being iteratively adjusted by a momentum factor based on whether a model score improves, the model score being calculated based on the actual data. The actions further include calculating a value score for the entity using the weighted predictive model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for determining unplanned hospitalization risk for a patient, the method being executed by one or more processors and comprising: receiving, by the one or more processors, input data defining a predictive model, the predictive model comprising a plurality of features; weighting, by the one or more processors, the predictive model iteratively for each feature, using hospitalization data comprising values for each feature for a plurality of patients that were previously hospitalized, by: iteratively adjusting a current weight for the feature by a momentum until the momentum equals zero, the momentum being iteratively adjusted by a factor of two based on whether a model score improves, the model score being calculated based on the hospitalization data; and calculating, by the one or more processors, a hospitalization risk score for the patient using the weighted predictive model. 2. The method of claim 1, wherein iteratively adjusting a current weight for the feature by a momentum comprises using previously determined weights for each feature as initial weights for each feature in a current iteration. 3. The method of claim 1, wherein the predictive model comprises one of an algebraic model and a tree-based model. 4. The method of claim 1, wherein the predictive model further comprises respective initial weights assigned to one or more features of the plurality of features. 5. The method of claim 4, wherein one or more of the respective initial weights equals 1. 6. The method of claim 4, wherein one or more of the respective initial weights equals 0. 7. The method of claim 1, wherein the plurality of features represent one or more of medical parameters and demographic parameters. 8. The method of claim 7, wherein the medical parameters comprise one or more of DSM codes, ICD-9 codes, ICD-10 codes, SNOMED codes, LOINC codes, RxNORM codes, CPT codes, and non-codified parameters. 9. The method of claim 1, further comprising generating the predictive model based on the input data. 10. The method of claim 1, wherein the momentum comprises an amount by which the current weight is adjusted. 11. The method of claim 1, wherein the model score is compared to a previously calculated best model score. 12. The method of claim 11, wherein the model score is based on a difference between a predicted value determined using the predictive model and an actual value determined from the hospitalization data. 13. The method of claim 12, wherein the model score improves when the difference for a current iteration decreases as compared to a difference corresponding to the previously calculated best model score. 14. The method of claim 1, wherein the current weight is adjusted in a positive direction. 15. The method of claim 1, wherein a momentum rule is applied to set the momentum to −1 if, for an initial momentum of 1, the model score does not improve by a threshold value. 16. The method of claim 15, wherein the current weight is adjusted in a negative direction. 17. The method of claim 1, wherein the current weight for the feature is adjusted by the momentum until a maximum number of iterations has been reached for adjusting the current weight. 18. The method of claim 1, wherein a momentum rule is applied to set the momentum to zero once the momentum returns to a value of 1 or −1 for the case where the model score no longer improves. 19. The method of claim 1, wherein iterations for determining the respective weights are limited to a maximum number of iterations for each feature of the plurality of features. 20. The method of claim 1, wherein the hospitalization risk score is provided to a medical records system. 21. The method of claim 1, wherein the hospitalization risk score is used by a service provider to formulate one or more services for the patient. 22. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for determining unplanned hospitalization risk for a patient, the operations comprising: receiving input data defining a predictive model, the predictive model comprising a plurality of features; weighting the predictive model iteratively for each feature, using hospitalization data comprising values for each feature for a plurality of patients that were previously hospitalized, by: iteratively adjusting a current weight for the feature by a momentum until the momentum equals zero, the momentum being iteratively adjusted by a factor of two based on whether a model score improves, the model score being calculated based on the hospitalization data; and calculating a hospitalization risk score for the patient using the weighted predictive model. 23. A system, comprising: one or more processors; and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for determining unplanned hospitalization risk for a patient, the operations comprising: receiving input data defining a predictive model, the predictive model comprising a plurality of features, weighting the predictive model iteratively for each feature, using hospitalization data comprising values for each feature for a plurality of patients that were previously hospitalized, by: iteratively adjusting a current weight for the feature by a momentum until the momentum equals zero, the momentum being iteratively adjusted by a factor of two based on whether a model score improves, the model score being calculated based on the hospitalization data, and calculating a hospitalization risk score for the patient using the weighted predictive model. 24. A method for predicting a value associated with an entity, the method being executed by one or more processors and comprising: receiving, by the one or more processors, input data defining a predictive model, the predictive model comprising a plurality of features; weighting, by the one or more processors, the predictive model iteratively for each feature, using actual data comprising values for each feature for a plurality of entities within a population, by: iteratively adjusting a current weight for the feature by a momentum until the momentum equals zero, the momentum being iteratively adjusted by a momentum factor based on whether a model score improves, the model score being calculated based on the actual data; and calculating, by the one or more processors, a value score for the entity using the weighted predictive model. 25. The method of claim 24, wherein iteratively adjusting a current weight for the feature by a momentum comprises using previously determined weights for each feature as initial weights for each feature in a current iteration. 26. The method of claim 24, wherein the predictive model comprises one of an algebraic model and a tree-based model. 27. The method of claim 24, wherein the predictive model further comprises respective initial weights assigned to one or more features of the plurality of features. 28. The method of claim 27, wherein one or more of the respective initial weights equals 1. 29. The method of claim 27, wherein one or more of the respective initial weights equals 0. 30. The method of claim 24, further comprising generating the predictive model based on the input data. 31. The method of claim 24, wherein the model score is compared to a previously calculated best model score. 32. The method of claim 31, wherein the model score is based on a difference between a predicted value determined using the predictive model and an actual value determined from the actual data. 33. The method of claim 32, wherein the model score improves when the difference for a current iteration decreases as compared to a difference corresponding to the previously calculated best model score. 34. The method of claim 24, wherein the current weight is adjusted in a positive direction. 35. The method of claim 24, wherein a momentum rule is applied to set the momentum to −1 if, for an initial momentum of 1, the model score does not improve by a threshold value. 36. The method of claim 24, wherein the current weight is adjusted in a negative direction. 37. The method of claim 24, wherein the current weight for the feature is adjusted by the momentum factor until a maximum number of iterations has been reached for adjusting the current weight. 38. The method of claim 24, wherein a momentum rule is applied to set the momentum to zero once the momentum returns to a value of 1 or −1 for the case where the model score no longer improves. 39. The method of claim 24, wherein iterations for determining the respective weights are limited to a maximum number of iterations for each feature of the plurality of features. 40. The method of claim 24, further comprising storing the predictive model to a computer-readable memory.