Cigna Intellectual Property, Inc.

Yee Wah Eva Lee

David Fogarty

Chit Ming Yip

Man Tat Lam

Yogendra D Bhosrekar

Chun Ho Chan

Xiao Xiao

Robert E Chudzik

Ho Fai Yau

Margaret A Shaw

Mahendra Bisht

Hei Fung

Sourav Maharana

Nicholas F Nett

Stephanie C Swain

Man Hin Wong

Yifei Luo

A computerized method of automatic distributed communication includes training a first and second machine learning models with historical feature vector inputs to generate a likelihood output and a mean count output, respectively. For each entity in a set, the method includes processing a likelihood feature vector input with the first machine learning model to generate a likelihood output indicative of a likelihood that the entity will have an avoidable negative health event within a specified first time period, and processing a mean count feature vector input with the second machine learning model to generate a mean count output indicative of an expected number of avoidable negative health events that the entity will have within a specified second time period. The method includes automatically distributing structured campaign data to at least a subset of entities in the set according to the likelihood output or the mean count output.

Machine learning systems for automated database element processing and prediction output generation

A computer system includes memory hardware configured to store a machine learning model, historical feature vector inputs, and computer-executable instructions, and processor hardware configured to execute the instructions. The instructions include training a first machine learning model with the historical feature vector inputs to generate a title score output, and training a second machine learning model with the historical feature vector inputs to generate a background score output. For each entity in a set, the instructions include processing a title feature vector input with the first machine learning model, and processing a background feature vector with a second machine learning model, to generate a tittle score output and a background score output each indicative of a likelihood that the entity is a decision entity. The instructions include automatically distributing structured campaign data to the entity based on the title score output and the background score output.

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South Windsor, CT, United States of America

Yee Wah Eva Lee