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公开(公告)号：US20230162830A1
公开(公告)日：2023-05-25
申请号：US17576055
申请日：2022-01-14
Applicant: Express Scripts Strategic Development, Inc.
Inventor： Viswanathan Subramanian , Mahipal Singareddy , Vincenzo N. Desantis , Sreenivasa Chennuru , Sravan Kumar Goud Golla , Camille Patel
CPC classification number: G16H20/10 , G06V30/1916 , G06V10/70 , G06V30/19147 , G06V30/41
Abstract: A computer system includes memory hardware configured to store a machine learning model, a record database, and historical feature vector inputs. Processor hardware is configured to execute instructions which include training the machine learning model to generate an entity field output, and for each of multiple database entities, scanning the database entity to generate a feature vector input, and processing the feature vector input to generate the entity field output. In response to determining that the entity field output includes at least one missing field value, the instructions include accessing the record database to identify a predicted value for the missing field value, analyzing the structured scan data or rescanning the database entity to determine whether the predicted value is present in the database entity, and assigning the database entity to the validated subset of the multiple database entities when the predicted value is present in the database entity.
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Patent Agency Ranking
2.发明申请公开(公告)号：US20230100574A1
公开(公告)日：2023-03-30
申请号：US17994442
申请日：2022-11-28
Applicant: Express Scripts Strategic Development, Inc.
Inventor： Sudipto Dey , Camille Patel , Pulla Reddy Yeduru , Robert Seyss
Abstract: 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.
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3.发明授权公开(公告)号：US11961598B1
公开(公告)日：2024-04-16
申请号：US16913797
申请日：2020-06-26
Applicant: Express Scripts Strategic Development, Inc.
Inventor： Morgan J. Finley , Garret L. Anderson , Camille Patel , Michael Nassar , Siju Vattakunnumpurath Eugin , Daniel Owens
CPC classification number: G16H20/10 , G06F16/283 , G06N20/00 , G16H40/20
Abstract: 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.
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4.发明授权公开(公告)号：US11848086B2
公开(公告)日：2023-12-19
申请号：US17994442
申请日：2022-11-28
Applicant: Express Scripts Strategic Development, Inc.
Inventor： Sudipto Dey , Camille Patel , Pulla R. Yeduru , Robert Seyss
Abstract: 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.
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公开(公告)号：US20240087709A1
公开(公告)日：2024-03-14
申请号：US18514181
申请日：2023-11-20
Applicant: Express Scripts Strategic Development, Inc.
Inventor： Sudipto Dey , Camille Patel , Pulla Reddy P. Yeduru , Robert A. Seyss
Abstract: Methods and systems for selecting a machine learning algorithm are described. In one embodiment, one or more factors to be used by a machine learning algorithm in predicting a value of a required pharmacy element of a prescription are identified, the machine learning algorithm is trained to predict the value of the required pharmacy element using a first subset of previously received prescriptions, a success rates for the machine learning algorithm 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 the machine learning algorithm predicts the value of the required pharmacy element of the prescription for a first predetermined period.
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6.发明授权公开(公告)号：US11515022B1
公开(公告)日：2022-11-29
申请号：US16272090
申请日：2019-02-11
Applicant: Express Scripts Strategic Development, Inc.
Inventor： Sudipto Dey , Camille Patel , Pulla R. Yeduru , Robert Seyss
Abstract: 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.
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