Cigna Intellectual Property, Inc.

Chun Ho Chan

David Fogarty

Chit Ming Yip

Yee Wah Eva Lee

Xiao Xiao

Mahendra Bisht

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

Growing community of inventors

Hong Kong, China

Chun Ho Chan