Characterizing subpopulations by their response to a given exposure relative to an alternative. Data for a set of subjects is received, including two exposures, two outcomes, and a set of characteristics. For a number of subsets an outcome model that estimates the probability of an outcome, given an exposure and the characteristics, is trained; an individual odds ratio (iOR), based on the outcome model, is computed; a sparse model that classifies subjects into a high-iOR group or another group is trained; the characteristics used in the sparse model are recorded; and, based on the recorded characteristics, a primary set of characteristics is selected. Another outcome model, based on the set of subjects and the characteristics, is trained. An iOR based on the other outcome model is computed. A sparse model for classifying subjects into a high-iOR group or another group, using the primary set of characteristics, is trained.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for characterizing subpopulations by their response to a given exposure relative to an alternative, the method comprising: receiving, by a computer, a global dataset comprising data for subjects associated with an exposure and an outcome, and population characteristics associated with the subjects, wherein the exposure is selected from the group consisting of a first exposure and a second exposure, and the outcome is selected from the group consisting of a first outcome and a second outcome; determining, by the computer, a primary set of population characteristics based on one or more sparse machine learning models respectively associated with one or more subsets of the global dataset, wherein the determining comprises: training one or more outcome machine learning models based on the one or more subsets, wherein an outcome machine learning model is trained on a respective subset, and the outcome machine learning model estimates a preliminary probability of the outcome, given the exposure and the population characteristics associated with the subjects in the subset; computing a preliminary individual odds ratio (iOR) for subjects in the one or more subsets, wherein the preliminary iOR is based on the preliminary probability estimated by the outcome machine learning model trained on the respective subset, and wherein the preliminary iOR measures odds of the first outcome being associated with the first exposure, relative to odds of the first outcome being associated with the second exposure; splitting the one or more subsets, respectively, into a preliminary high-iOR group and a preliminary further group; training one or more sparse machine learning models based on the one or more subsets, wherein a sparse machine learning model is trained on the respective subset, and the sparse machine learning model classifies the subjects in the respective subset into the preliminary high-iOR group or the preliminary further group; recording population characteristics used in the one or more sparse machine learning models trained on the one or more subsets; and selecting the primary set of population characteristics based on the recorded population characteristics used in the one or more sparse machine learning models trained on the one or more subsets; and creating, by the computer, a primary sparse machine learning model based on the primary set of population characteristics, wherein the creating comprises: training another outcome machine learning model based on the global dataset, wherein the another outcome machine learning model estimates a primary probability of the outcome, given the exposure and the population characteristics associated with the subjects in the global dataset; computing a primary iOR for subjects in the global dataset, wherein the primary iOR is based on the primary probability estimated by the another outcome machine learning model trained on the global dataset, wherein the primary iOR measures odds of the first outcome being associated with the first exposure, relative to odds of the first outcome being associated with the second exposure; splitting the global dataset into a high-iOR group and a further group; and training the primary sparse machine learning model based on the global dataset, wherein the primary sparse machine learning model classifies subjects in the global dataset into the high-iOR group or the further group based on the primary set of population characteristics. 2. A method in accordance with claim 1, wherein the one or more subsets of the global dataset comprise a predetermined number of subsets of the global dataset, wherein each subset comprises a predetermined percentage of subjects from the global dataset. 3. A method in accordance with claim 1, wherein the data for the subjects associated with the exposure and the outcome is observational data from a non-randomized study. 4. A method in accordance with claim 1, wherein training the one or more outcome machine learning models comprises binary classification based on logistic regression. 5. A method in accordance with claim 1, wherein training the one or more sparse machine learning models comprises binary classification based on logistic regression. 6. A method in accordance with claim 1, wherein training the another outcome machine learning models comprises binary classification based on logistic regression. 7. A method in accordance with claim 1, wherein training the primary sparse machine learning model comprises binary classification based on logistic regression. 8. A method in accordance with claim 1, wherein splitting the one or more subsets, respectively, into the preliminary high-iOR group and the preliminary further group comprises splitting the respective subsets based on a threshold that is one of: the median iOR of the subset; the mean iOR of the subset; or a predetermined iOR value. 9. A method in accordance with claim 1, wherein splitting the global dataset into the high-iOR group and the further group comprises splitting the global dataset based on a threshold that is one of: the median iOR of the subset; the mean iOR of the subset; or a predetermined iOR value. 10. A method in accordance with claim 1, wherein training the one or more sparse machine learning models further comprises a population characteristics selection method selected from the group consisting of forward selection or stepwise selection. 11. A method in accordance with claim 1, wherein selecting the primary set of population characteristics based on the recorded population characteristics comprises: selecting recorded population characteristics associated with at least a predetermined percentage of the subsets. 12. A method in accordance with claim 1, wherein the preliminary further group is a disjoint group complementing the preliminary high-iOR group. 13. A method in accordance with claim 1, wherein the further group is a disjoint group complementing the high-iOR group. 14. A method in accordance with claim 1, wherein the one or more subsets are randomly selected. 15. A computer system for characterizing subpopulations by their response to a given exposure relative to an alternative, the computer system comprising: one or more computer processors, one or more non-transitory computer-readable storage media, and program instructions stored on one or more of the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions to receive a global dataset comprising data for subjects associated with an exposure and an outcome, and population characteristics associated with the subjects, wherein the exposure is selected from the group consisting of a first exposure and a second exposure, and the outcome is selected from the group consisting of a first outcome and a second outcome; program instructions to determine a primary set of population characteristics based on one or more sparse machine learning models respectively associated with one or more subsets of the global dataset, comprising: training one or more outcome machine learning models based on the one or more subsets, wherein an outcome machine learning model is trained on a respective subset, and the outcome machine learning model estimates a preliminary probability of the outcome, given the exposure and the population characteristics associated with the subjects in the subset; computing a preliminary individual odds ratio (iOR) for subjects in the one or more subsets, wherein the preliminary iOR is based on the preliminary probability estimated by the outcome machine learning model trained on the respective subset, and wherein the preliminary iOR measures odds of the first outcome being associated with the first exposure, relative to odds of the first outcome being associated with the second exposure; splitting the one or more subsets, respectively, into a preliminary high-iOR group and a preliminary further group; training one or more sparse machine learning models based on the one or more subsets, wherein a sparse machine learning model is trained on the respective subset, and the sparse machine learning model classifies the subjects in the respective subset into the preliminary high-iOR group or the preliminary further group; recording population characteristics used in the one or more sparse machine learning models trained on the one or more subsets; and selecting the primary set of population characteristics based on the recorded population characteristics used in the one or more sparse machine learning models trained on the one or more subsets; and program instructions to create a primary sparse machine learning model based on the primary set of population characteristics comprising: training another outcome machine learning model based on the global dataset, wherein the another outcome machine learning model estimates a primary probability of the outcome, given the exposure and the population characteristics associated with the subjects in the global dataset; computing a primary iOR for subjects in the global dataset, wherein the primary iOR is based on the probability of the outcome estimated by the another outcome machine learning model trained on the global dataset, wherein the primary iOR measures odds of the first outcome being associated with the first exposure, relative to odds of the first outcome being associated with the second exposure; splitting the global dataset into a high-iOR group and a further group; and training the primary sparse machine learning model based on the global dataset, wherein the primary sparse machine learning model classifies subjects in the global dataset into the high-iOR group or the further group based on the primary set of population characteristics. 16. A computer system in accordance with claim 15, wherein the one or more subsets of the global dataset comprise a predetermined number of subsets of the global dataset, wherein each subset comprises a predetermined percentage of subjects from the global dataset. 17. A computer system in accordance with claim 15, wherein training the one or more outcome machine learning models comprises binary classification based on logistic regression. 18. A computer system in accordance with claim 15, wherein training the one or more sparse machine learning models comprises binary classification based on logistic regression, and wherein training the one or more sparse machine learning models further comprises a population characteristics selection method selected from the group consisting of forward selection or stepwise selection. 19. A computer system in accordance with claim 15, wherein splitting the one or more subsets, respectively, into the preliminary high-iOR group and the preliminary further group comprises splitting the respective subsets based on a threshold that is one of: the median iOR of the subset; the mean iOR of the subset; or a predetermined iOR value. 20. A computer system in accordance with claim 15, wherein selecting the primary set of population characteristics based on the recorded population characteristics comprises selecting recorded population characteristics associated with at least a predetermined percentage of the subsets.