Express Scripts Strategic Development, Inc.

Camille Patel

Sudipto Dey

Robert Seyss

Garret L Anderson

Morgan J Finley

Pulla R Yeduru

Daniel Owens

Nihitha Davis

Heather Durosko

Michael Nassar

Siju Vattakunnumpurath Eugin

A machine learning server for trained prediction of unenrollment in claim processing systems is provided. The machine learning server includes a processor configured to construct a layered neural network configured to be trained by claim features. The processor is also configured to receive historic claims. The processor is also configured to extract a set of historic claim features from the historic claim data of the historic claims. The processor is further configured to obtain the claim enrollment status associated with the sets of historic claim features. The processor is also configured to train the layered neural network with the set of historic claim features and the claim enrollment status to predict the claim enrollment status of a pending claim. The processor is further configured to receive a pending claim and to apply the pending claim features to the trained layered neural network to obtain a predicted claim enrollment status.

Machine learning systems for predicting unenrollment in claims processing

A method for predicting errors in prescription claim data is performed by a claim analysis device. The method includes extracting historical claim features from successfully processed historical claims received from the data warehouse system. The method includes extracting pending claim features from a pending claim. The method includes applying a binarization process on the extracted historical claim features to obtain a binarized training feature set. The method includes applying the binarization process on the extracted pending claim features to obtain a binarized pending feature set. The method includes calculating an aggregate distance between the binarized pending feature set and the binarized training feature set. The method includes identifying the historical claim associated with the least aggregate distance as a predictive historical claim. The method includes transmitting an alert upon determining that a billing attribute of the predictive historical claim fails to match a corresponding billing attribute of the pending claim.

Machine learning systems for error detection in data processing systems and related methods

Methods and systems for selecting a machine learning algorithm are described. In one embodiment, one or more factors to be used by a plurality of machine learning algorithms in predicting a value of a required pharmacy element of a prescription are identified, each of the plurality of machine learning algorithms are trained to predict the value of the required pharmacy element using a first subset of previously received prescriptions, respective success rates for each of the plurality of machine learning algorithms at predicting respective known values of respective known required pharmacy elements for each of a second subset of the previously received prescriptions are determined, and a first of the plurality of machine learning algorithms having a highest success rate is selected to predict the value of the required pharmacy element of the prescription for a first predetermined period.

Methods and systems for automatic prescription processing using machine learning algorithm

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St. Louis, MO, United States of America

Camille Patel