Express Scripts Strategic Development, Inc.

Robert Seyss

Sudipto Dey

Pulla Reddy P Yeduru

Camille Patel

Pulla R Yeduru

Jasbir Singh

A computer system includes memory hardware configured to store structured microservice configuration data having multiple microservice entries each associated with one of multiple microservice applications of a request processing architecture. The system includes processor hardware configured to access structured microservice configuration data to identify the microservice applications of the request processing architecture, subscribing to messages transmitted by the identified microservice applications for event monitoring, and receiving multiple messages transmitted by the identified microservice applications. For each of the multiple received messages, the instructions include analyzing one or more fields of the received message to determine a correlation identifier associated with the received message, identifying one of the multiple request data structures, storing an event message entry in the identified request data structure, and transforming a user interface of a user device to display at least a portion of the multiple event message entries.

Microservice architecture with automated non-intrusive event tracing

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.

Methods and systems for selecting a machine learning algorithm

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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St. Louis, MO, United States of America

Robert Seyss