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

IPC: G16H20/10 ,  G16H50/70 ,  G06N20/20

CPC classification number: G16H20/10 ,  G06N20/20 ,  G16H50/70

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.

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

IPC: G16H50/70 ,  G06N20/20 ,  G16H20/10

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.