US 12,230,380 B2
Machine learning techniques for prospective event-based classification
Jeffrey Smith, Prior Lake, MN (US); Marissa N. Dent, Homer Glen, IL (US); Louis A. Wedge, Stillwater, MN (US); Cary R. Shelley, Catonsville, MD (US); and Aliya Mansoor, Columbia, MD (US)
Assigned to UnitedHealth Group Incorporated, Minnetonka, MN (US)
Filed by UnitedHealth Group Incorporated, Minnetonka, MN (US)
Filed on Feb. 21, 2022, as Appl. No. 17/651,873.
Claims priority of provisional application 63/261,496, filed on Sep. 22, 2021.
Prior Publication US 2023/0086384 A1, Mar. 23, 2023
Int. Cl. G06F 18/2431 (2023.01); G16H 20/10 (2018.01)
CPC G16H 20/10 (2018.01) [G06F 18/2431 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
generating, by one or more processors and based at least in part on one or more predictive input events occurring during an observation period of a predictive timeframe associated with a predictive entity, a current coverage score for the observation period;
generating, by the one or more processors executing a prospective coverage score determination machine learning model comprising one or more neural network layers and trained based at least in part on historically observed coverage scores for one or more historical timeframes, a prospective coverage score for the predictive timeframe based at least in part on a group of prospective coverage model input features comprising an event distribution feature value associated with the one or more predictive input events and a predictive entity feature value associated with the predictive entity;
generating, by the one or more processors and based at least in part on a prospective coverage deviation measure for the prospective coverage score and a threshold coverage measure, a prospective coverage gap score for the predictive timeframe;
inputting, by the one or more processors and into a prospective event-based classification machine learning model, the current coverage score, the prospective coverage score, and the prospective coverage gap score as a group of classification input features, causing the prospective event-based classification machine learning model to generate a prospective event-based classification that is indicative of a prospective outcome associated with the predictive entity for the predictive timeframe, wherein the prospective event-based classification machine learning model is trained using a training set comprising a plurality of training entries each having a classification label; and
performing, by the one or more processors, one or more prediction-based actions based at least in part on the prospective event-based classification.