Predictive machine learning system for early identification and recommendation of strategic interventions to improve participant outcomes

Abstract

A system and method are presented in this invention for a machine learning model configured to generate recommendations to improve the probability of a desired outcome for participants in a program. Individual profile data for past and current participants, which includes participant attributes derived internal and external to the program, are used to conduct a series of assessments of the participant population to determine the probability of participants to achieve the desired outcome(s). Inputs to the assessments include the output(s) of the previous assessment(s) conducted in the series. Past participants with the undesired and desired results are assessed to identify detrimental and beneficial impactors to teach the system about the specific participant population(s) based on the plurality of attributes within the participant profiles. Individually tailored recommendations are automatically generated for current participants as a function of the identified impactors and tracked to further refine future recommendations generated by the system.

Dependencies are inferred from the claim text.