Techniques to provide real-time processing enhancements and modeling for data anomaly detection pertaining to medical events using decision trees

Abstract

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.