Embodiments are generated directed to method, medium, and system including processing circuitry to generate records including randomly selected events for each of one or more subjects having one or more of the same category parameters as a subject of a particular event. The processing circuitry may also present, on a display device, a computer-generated model based on the records, the model having a decision tree data structure having decision tree nodes corresponding with historical events from the records, each of the decision tree nodes having an indication of a likelihood of occurrence for the particular event based on whether a corresponding history event of the decision tree node occurred or did not occur within a specific time period. Embodiments of the real-time distributed nature of the systems and processing discussed herein can solve big data analytics processing problems and facilitate data anomaly detection.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. (canceled) 2. An apparatus, comprising: processing circuitry; and logic, at least partially implemented by the processing circuitry, the logic to: obtain medical event history information for subjects, the medical event history information comprising medical history events; determine a particular medical event to determine a probability of occurrence based on a presence or absence of medical history events at specific prior time periods; select, at random, one medical event per subject for subjects having experienced the particular medical event for inclusion in a set of medical history events to be modeled; select, at random, one medical event per subject for subjects having not experienced the particular medical event for inclusion in the set of medical history events to be modeled, each of the medical events selected for subjects having not experienced the particular medical event having a same seasonality as the medical events selected for subject having experienced the particular medical event; generate a model based on the set medical history events for subjects having experienced the particular medical event and having not experienced the particular medical event, the model comprising a decision tree having nodes corresponding with medical history events from the set of medical events, each of the nodes having an indication of a probability of occurrence for the particular medical event based on whether the corresponding medical history event of the node occurred or did not occur within a specific time period; and present the decision tree having the nodes on a display. 3. The apparatus of claim 2, the particular medical event associated with multiple category parameters comprising at least one of gender, age, and eligibility category and the model generated for the multiple category parameters. 4. The apparatus of claim 2, the medical event history information comprising claim codes, subject identifiers, and medical history event dates for the subjects. 5. The apparatus of claim 2, the logic to reduce a number of medical history events by grouping two or more claim codes corresponding with a single medical history event into a group claim code. 6. The apparatus of claim 2, the logic to include a medical history event date for each of the medical history events in the set of medical history events, each medical history event date to indicate a date of last occurrence for the particular medical event or a null value for each subject having not experienced the particular medical history event. 7. The apparatus of claim 2, wherein at least one of the medical history events of the set of medical history events is a different type of event than the particular medical event. 8. The apparatus of claim 2, the logic to determine occurrence indicators for each medical history event in the set of medical history events, each of the occurrence indicators to indicate if a subject associated with the particular medical event experienced each medical history event. 9. The apparatus of claim 8, the logic to determine period indicators for each of the medical history events indicated as occurring to the subject by the occurrence indicators, each of the period indicators to indicate if a medical history event occurred within a specified period of time. 10. The apparatus of claim 9, the decision tree comprising decision branches coupling the nodes, each decision branch based on the occurrence indicators and the period indicators. 11. The apparatus of claim 2, the logic to generate the model and determine probability of occurrence for the particular medical event in real-time. 12. A computer-implemented method, comprising: obtaining medical event history information for subjects, the medical event history information comprising medical history events; determining a particular medical event to determine a probability of occurrence based on a presence or absence of medical history events at specific prior time periods; selecting, at random, one medical event per subject for subjects having experienced the particular medical event for inclusion in a set of medical events to be modeled; selecting, at random, one medical event per subject for subjects having not experienced the particular medical event for inclusion in the set of medical events to be modeled, each of the medical events selected for subjects having not experienced the particular medical event having a same seasonality as the medical events selected for subject having experienced the particular medical event; generating a model based on the set medical events for subjects having experienced the particular medical event and having not experienced the particular medical event, the model comprising a decision tree having nodes corresponding with medical history events from the set of medical events, each of the nodes having an indication of a probability of occurrence for the particular medical event based on whether the corresponding medical history event of the node occurred or did not occur within a specific time period; and presenting the decision tree having the nodes on a display. 13. The computer-implemented method of claim 12, the particular medical event associated with multiple category parameters comprising at least one of gender, age, and eligibility category and the model generated for the multiple category parameters. 14. The computer-implemented method of claim 12, the medical event history information comprising claim codes, subject identifiers, and medical history event dates for the subjects. 15. The computer-implemented method of claim 12, comprising reducing a number of medical history events by grouping two or more claim codes corresponding with a single medical history event into a group claim code. 16. The computer-implemented method of claim 12, comprising including a medical history event date for each of the medical history events in the set of medical history events, each medical history event date to indicate a date of last occurrence for the particular medical event or a null value for each subject having not experienced the particular medical history event. 17. The computer-implemented method of claim 12, wherein at least one of the medical history events of the set of medical events is a different type of event than the particular medical event. 18. The computer-implemented method of claim 12, comprising determining occurrence indicators for each medical history event in the set of medical events, each of the occurrence indicators to indicate if a subject associated with the particular medical event experienced each medical history event. 19. The computer-implemented method of claim 18, comprising determining period indicators for each of the medical history events indicated as occurring to the subject by the occurrence indicators, each of the period indicators to indicate if a medical history event occurred within a specified period of time. 20. The computer-implemented method of claim 19, the decision tree comprising decision branches coupling the nodes, each decision branch based on the occurrence indicators and the period indicators. 21. The computer-implemented method of claim 12, comprising generating the model and determine probability of occurrence for the particular medical event in real-time. 22. At least one non-transitory computer-readable storage medium comprising instructions that when executed cause processing circuitry to: obtain medical event history information for subjects, the medical event history information comprising medical history events; determine a particular medical event to determine a probability of occurrence based on a presence or absence of medical history events at specific prior time periods; select, at random, one medical event per subject for subjects having experienced the particular medical event for inclusion in a set of medical history events to be modeled; select, at random, one medical event per subject for subjects having not experienced the particular medical event for inclusion in the set of medical history events to be modeled, each of the medical events selected for subjects having not experienced the particular medical event having a same seasonality as the medical events selected for subject having experienced the particular medical event; generate a model based on the set medical history events for subjects having experienced the particular medical event and having not experienced the particular medical event, the model comprising a decision tree having nodes corresponding with medical history events from the set of medical history events, each of the nodes having an indication of a probability of occurrence for the particular medical event based on whether the corresponding medical history event of the node occurred or did not occur within a specific time period; and present the decision tree having the nodes on a display. 23. The non-transitory computer-readable storage medium of claim 22, the particular medical event associated with multiple category parameters comprising at least one of gender, age, and eligibility category and the model generated for the multiple category parameters. 24. The non-transitory computer-readable storage medium of claim 22, the medical event history information comprising claim codes, subject identifiers, and medical history event dates for the subjects. 25. The non-transitory computer-readable storage medium of claim 22, comprising instructions that when executed cause the processing circuitry to reduce a number of medical history events by grouping two or more claim codes corresponding with a single medical history event into a group claim code. 26. The non-transitory computer-readable storage medium of claim 22, comprising instructions that when executed cause the processing circuitry to include a medical history event date for each of the medical history events in the set of medical history events, each medical history event date to indicate a date of last occurrence for the particular medical event or a null value for each subject having not experienced the particular medical event. 27. The non-transitory computer-readable storage medium of claim 22, wherein at least one of the medical history events of the set of medical history events is a different type of event than the particular medical event. 28. The non-transitory computer-readable storage medium of claim 22, comprising instructions that when executed cause the processing circuitry to determine occurrence indicators for each medical history event in the set of medical history events, each of the occurrence indicators to indicate if a subject associated with the particular medical event experienced each medical history event. 29. The non-transitory computer-readable storage medium of claim 28, comprising instructions that when executed cause the processing circuitry to determine period indicators for each of the medical history events indicated as occurring to the subject by the occurrence indicators, each of the period indicators to indicate if a medical history event occurred within a specified period of time. 30. The non-transitory computer-readable storage medium of claim 29, the decision tree comprising decision branches coupling the nodes, each decision branch based on the occurrence indicators and the period indicators. 31. The non-transitory computer-readable storage medium of claim 22, comprising instructions that when executed cause the processing circuitry to generate the model and determine probability of occurrence for the particular medical event in real-time.