Express Scripts Strategic Development, Inc.

Pulla Reddy P Yeduru

Sudipto Dey

Robert Seyss

Camille Patel

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 predicting prescription drug directions are described. In one embodiment, a drug direction prediction subsystem receives and pre-processes values of a plurality of required pharmacy elements for a corresponding prescription of a plurality of prescriptions, generates respective weights for the values of the plurality of required pharmacy elements of the prescription based on one or more of the values of the plurality required pharmacy elements of the prescription, creates a machine learning model to be used by the applicable one of the plurality of machine learning algorithms in predicting drug directions of the prescription, the machine learning model using the values of the plurality of required pharmacy elements of the prescription and the respective weights, and predicts a plurality of drug directions of a new prescription by executing the machine learning model using weighted values of the plurality of required pharmacy elements of the new prescription.

Methods and systems for predicting prescription directions using machine learning algorithm

Methods and systems for predicting drug directions of a prescription are described. In one embodiment, values of a plurality of required pharmacy elements of a corresponding prescription are received and pre-processed, the values are weighted, a machine learning model to be used by a plurality of machine learning algorithms in predicting drug directions of the prescription is created, and a plurality of drug directions of the prescription are predicted by executing the machine learning algorithms on the weighted values of the plurality of required pharmacy elements of the prescription.

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Austin, TX, United States of America

Pulla Reddy P Yeduru