A system disclosed herein includes a hardware processor and a predictive risk model training software code stored in a system memory. The hardware processor executes the software code to receive vital sign data of a population of subjects including positive and negative subjects with respect to a health state, to define data sets for use in training a predictive risk model, to transform the vital sign data to parameters characterizing the vital sign data, and to obtain differential parameters based on those parameters. The hardware processor executes the software code to further generate combinatorial parameters using the parameters and the differential parameters, to analyze the parameters, the differential parameters, and the combinatorial parameters to identify a reduced set of parameters correlated with the health state, to identify a predictive set of parameters enabling prediction of the health state for a living subject, and to compute predictive risk model coefficients.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for training a predictive risk model, the system comprising: a hardware processor and a system memory; a predictive risk model training software code stored in the system memory; wherein the hardware processor is configured to execute the predictive risk model training software code to: receive a vital sign data of each subject of a population of subjects including positive subjects and negative subjects with respect to a health state; define data sets for use in training the predictive risk model; transform the vital sign data to a first plurality of parameters characterizing the vital sign data; obtain a second plurality of differential parameters based on the first plurality of parameters; generate a third plurality of combinatorial parameters using the first plurality of parameters and the second plurality of differential parameters; analyze the first plurality of parameters, the second plurality of differential parameters, and the third plurality of combinatorial parameters to identify a reduced set of plurality of parameters correlated with the health state; identify, from among the reduced set of plurality of parameters, a predictive set of parameters enabling prediction of the health state for a living subject; and compute predictive risk model coefficients, thereby training the predictive risk model. 2. The system of claim 1, wherein each of the third plurality of combinatorial parameters comprises a power combination of a subset of the first plurality of parameters and the second plurality of differential parameters. 3. The system of claim 2, wherein the power combination includes integer powers from among negative two, negative one, zero, one, and two (−2, −1, 0, 1, 2). 4. The system of claim 1, wherein each of the third plurality of combinatorial parameters comprises a power combination of three parameters from the first plurality of parameters and the second plurality of differential parameters. 5. The system of claim 1, wherein the reduced set of plurality of parameters correlated with the health state are identified through a receiver operating characteristic (ROC) analysis of the first plurality of parameters, the second plurality of differential parameters, and the third plurality of combinatorial parameters. 6. The system of claim 1, wherein the predictive set of parameters is identified by sequentially testing predictions of the health state produced using each of the reduced set of plurality of parameters. 7. The system of claim 1, wherein the vital sign data comprises arterial pressure data, and the health state is hypotension. 8. A method for use by a system for training a predictive risk model, the system including a hardware processor and a predictive risk model training software code stored in a system memory, the method comprising: receiving, by the predictive risk model training software code executed by the hardware processor, a vital sign data of each subject of a population of subjects including positive subjects and negative subjects with respect to a health state; defining, by the predictive risk model training software code executed by the hardware processor, data sets for use in training the predictive risk model; transforming, by the predictive risk model training software code executed by the hardware processor, the vital sign data to a first plurality of parameters characterizing the vital sign data; obtaining, by the predictive risk model training software code executed by the hardware processor, a second plurality of differential parameters based on the first plurality of parameters; generating, by the predictive risk model training software code executed by the hardware processor, a third plurality of combinatorial parameters using the first plurality of parameters and the second plurality of differential parameters; analyzing, by the predictive risk model training software code executed by the hardware processor, the first plurality of parameters, the second plurality of differential parameters, and the third plurality of combinatorial parameters to identify a reduced set of plurality of parameters correlated with the health state; identifying from among the reduced set of plurality of parameters, by the predictive risk model training software code executed by the hardware processor, a predictive set of parameters enabling prediction of the health state for a living subject; and computing, by the predictive risk model training software code executed by the hardware processor, predictive risk model coefficients, thereby training the predictive risk model. 9. The method of claim 8, wherein each of the third plurality of combinatorial parameters comprises a power combination of a subset of the first plurality of parameters and the second plurality of differential parameters. 10. The method of claim 9, wherein the power combination includes integer powers from among negative two, negative one, zero, one, and two (−2, −1, 0, 1, 2). 11. The method of claim 8, wherein each of the third plurality of combinatorial parameters comprises a power combination of three parameters from the first plurality of parameters and the second plurality of differential parameters. 12. The method of claim 8, wherein analyzing the first plurality of parameters, the second plurality of differential parameters, and the third plurality of combinatorial parameters to identify the reduced set of plurality of parameters correlated with the health state includes performing a receiver operating characteristic (ROC) analysis of the first plurality of parameters, the second plurality of differential parameters, and the third plurality of combinatorial parameters. 13. The method of claim 8, wherein identifying the predictive set of parameters includes sequentially testing predictions of the health state produced using each of the reduced set of plurality of parameters. 14. The method of claim 8, wherein the vital sign data comprises arterial pressure data, and the health state is hypotension. 15. A computer-readable non-transitory medium having stored thereon instructions, which when executed by a hardware processor, instantiate a method comprising: receiving a vital sign data of each subject of a population of subjects including positive subjects and negative subjects with respect to a health state; defining data sets for use in training a predictive risk model; transforming the vital sign data to a first plurality of parameters characterizing the vital sign data; obtaining a second plurality of differential parameters based on the first plurality of parameters; generating a third plurality of combinatorial parameters using the first plurality of parameters and the second plurality of differential parameters; analyzing the first plurality of parameters, the second plurality of differential parameters, and the third plurality of combinatorial parameters to identify a reduced set of plurality of parameters correlated with the health state; identifying, from among the reduced set of plurality of parameters, a predictive set of parameters enabling prediction of the health state for a living subject; and computing predictive risk model coefficients, thereby training the predictive risk model. 16. The computer-readable non-transitory medium of claim 15, wherein each of the third plurality of combinatorial parameters comprises a power combination of a subset of the first plurality of parameters and the second plurality of differential parameters. 17. The computer-readable non-transitory medium of claim 16, wherein the power combination includes integer powers from among negative two, negative one, zero, one, and two (−2, −1, 0, 1, 2). 18. The computer-readable non-transitory medium of claim 15, wherein each of the third plurality of combinatorial parameters comprises a power combination of three parameters from the first plurality of parameters and the second plurality of differential parameters. 19. The computer-readable non-transitory medium of claim 18, wherein analyzing the first plurality of parameters, the second plurality of differential parameters, and the third plurality of combinatorial parameters to identify the reduced set of plurality of parameters correlated with the health state includes performing a receiver operating characteristic (ROC) analysis of the first plurality of parameters, the second plurality of differential parameters, and the third plurality of combinatorial parameters. 20. The computer-readable non-transitory medium of claim 15, wherein identifying the predictive set of parameters includes sequentially testing predictions of the health state produced using each of the reduced set of plurality of parameters.