Patent ID: 11875409
Assignee: DATA-CORE SYSTEMS, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A method performed by a computing system, the method comprising:
for an insurance claim comprising a plurality of features, wherein the plurality of features comprises one or more diagnosis codes and one or more procedure codes, determining that the insurance claim comprises a potential anomaly based at least in part on whether the insurance claim has a classification determined by an Artificial Intelligence/Machine Learning (AI/ML) engine below a threshold;
in response to the determining that the insurance claim comprises the potential anomaly, (i) for each feature of the insurance claim, (a) determining a sensitivity value of the feature relative to each other feature of the insurance claim, and (b) determining a saliency value of the feature relative to one or more categories relevant to the insurance claim, and (ii) estimating a value of the insurance claim based on a multi-variate linear regression model generated at least in part from a dataset of known valid insurance claims; and
based on the determined sensitivity values, the determined saliency values, and the estimated value of the insurance claim, recommending one or more ways to cure the potential anomaly based on results from one or both of (i) a First Model based recommendation or (ii) a Second Model based recommendation;
wherein the determining that the insurance claim comprises the potential anomaly based at least in part on whether the insurance claim has the classification determined by the AI/ML engine below the threshold comprises using the AI/ML engine to:
provide the insurance claim to both a neural network machine learning model and a co-occurrence matrix machine learning model; and
determine the classification of the insurance claim based on: (a) a first result returned by the neural network machine learning model, (b) a second result returned by the co-occurrence matrix machine learning model, and (c) one or more confidence levels associated with the neural network machine learning model and the co-occurrence matrix machine learning model; and

wherein either:
(1) for each feature of the insurance claim, the determining the sensitivity value of the feature relative to each other feature of the insurance claim comprises looking up the feature sensitivity for each feature in a feature-to-feature sensitivity database comprising sensitivities of features as a function of other features based on the dataset of known valid insurance claims, or
(2) for each feature of the insurance claim, the determining the saliency value of the feature relative to the one or more categories relevant to the insurance claim comprises looking up the saliency value of each feature in a feature-to-category saliency database comprising saliencies of features as a function of categories of insurance claims.