Abstract: Disclosed embodiments include determining whether the insurance claim comprises an anomaly based on whether the claim violates a rule in a Rules Engine or is scored by an Artificial Intelligence/Machine Learning engine below a threshold. When the claim comprises an anomaly, (i) for each feature of the claim, (a) determining a sensitivity value of the feature relative to each other feature of the claim, and (b) determining a saliency value of the feature relative categories relevant to the claim, and (ii) estimating a value of the claim based on a multi-variate linear regression model. And based on the sensitivity values, saliency values, and estimated value, recommending one or more ways to cure the anomaly based on (i) a Local Interpretable Model-Agnostic Explanations based recommendation and/or (ii) a Global Surrogate Model based recommendation.

Abstract: Systems and methods for generating a dataset of synthesized data items from a dataset of original data items are disclosed herein. Some embodiments include (i) selecting an original data item from the dataset of original data items, where each original data item (a) comprises a combination of first-type codes and second-type codes, and (b) is associated with a topic in a topic model; and (ii) generating a synthesized data item based on the original data item and the topic associated with the original data item, where the synthesized data item comprises a combination of first-type codes and second-type codes that differs from the combination of first-type codes and second-type codes in the original data item by one first-type code or one second-type code.

Abstract: Disclosed embodiments include generating invalid insurance claims based on category pairs from a category matrix derived from a set of known valid insurance claims, wherein an individual invalid insurance claim comprises at least one of: (i) a diagnosis code from a first category of a category pair and a procedure code from a second category of the category pair not in the first category or (ii) a procedure code from the first category of the category pair and a diagnosis code from the second category of the category pair not in the first category. Some embodiments also include generating a training dataset comprising (i) valid insurance claims from the set of known valid insurance claims and (ii) the invalid insurance claims; and using the training dataset to train an Artificial Intelligence/Machine Learning Engine to classify insurance claims as valid or invalid.

Abstract: Systems and methods for analyzing, segmenting, and classifying multimedia material are disclosed herein. Embodiments include (i) receiving multimedia material for analysis, (ii) extracting elements from the multimedia material and forming objects comprising the elements; (iii) segmenting the multimedia material into segments, where individual segments include objects located within a threshold distance from each other; (iv) detecting objects within each segment; (v) associating attributes with the detected objects within the segments; (vi) annotating the segments by creating a relationship tree among the objects within each segment; and (vii) storing annotations of the segments for analysis.