Cigna Intellectual Property, Inc.

Mahendra Bisht

David Fogarty

Chit Ming Yip

Yee Wah Eva Lee

Xiao Xiao

Chun Ho Chan

Yifei Luo

Ho Fai Yau

Hei Fung

Nicholas F Nett

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

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Bengaluru, India

Mahendra Bisht