Data-core Systems, Inc.

Jishnu Bhattacharyya

Sin-Min Chang

Anshuman Narayan

Pradeep K Banerjee

Rathi Dasgupta

Dhrubajyoti Chakravarty

Embodiments for automatically converting printed documents into electronic format using artificial intelligence techniques disclosed herein include: (i) receiving a plurality of images of documents; (ii) for each received image, using an image classification algorithm to classify the image as one of (a) an image of a first type of document, or (b) an image of a second type of document; (iii) for each image classified as an image of the first type of document, using an object localization algorithm to identity an area of interest in the image; (iv) for an identified area of interest, using an optical character recognition algorithm to extract text from the identified area of interest; and (v) populating a record associated with the document with the extracted text.

Systems, methods, and devices for automatically converting explanation of benefits (EOB) printable documents into electronic format using artificial intelligence techniques

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.

Systems and methods for identifying and curing anomalies in insurance claims

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.

Synthesizing data based on topic modeling for training and testing machine learning systems

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.

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Philadelphia, PA, United States of America

Jishnu Bhattacharyya