Included Health, Inc.

Twitter, Inc.

Grand Rounds, Inc.

Zachary Taylor

Peyton Rose

Adam M Singer

Nathaniel Freese

William Morgan

Eric Carlson

Buster Benson

Jeremy Gordon

Ian Chan

Reeve Thompson

Brian Ellin

Grant Monroe

Russell D'Sa

Stephen Martin

Meera Rao

Brian Guarraci

Sameer Soi

Molong Li

Ye Wang

Luke Alonso

Rick Wolf

Zhigang Chen

Ramakrishna Soma

Jacob David Rifkin

Carolina Marquez

Zhijang Chen

Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.

Systems and methods for machine learning models for performance measurement

Methods, systems, and computer-readable media for multi-domain, multi-modal data segmentation, and automatically generating and refining hypotheses. The method receives data from a plurality of data sources; synthesizing the receive data; identifying trigger event data based on the synthesized data; generating an episode based on a segmentation of the synthesized data and trigger event data; and identifying at least one set of observational features associated with the episode based on the synthesized data and a relevancy metric. The method also includes iteratively generating a hypothesis based on the observational features using machine learning, predicting an outcome based on the hypothesis using machine learning, generating an outcome measure, and validating the hypothesis based on the outcome measure. The method also includes determining an optimal hypothesis upon reaching the threshold value; analyzing coefficients associated with the optimal hypothesis; and identifying a set of factors associated based on the analyzed coefficients.

Systems and methods for multi-domain data segmentation, automatic hypotheses generation and outcome optimization

Methods, systems, and computer-readable media for generating a personalized action recommendation are provided. The method acquires a request for a service that is associated with a user and the user's condition. The method then identifies one or more features of the user based on stored user information. The method next assigns the user to a segment based on the identified one or more features, generates a set of one or more recommended actions for the user based on the segment, and determines an expected value of each of the one or more recommended actions. The method determines a rank of the one or more recommended actions based on the expected value of each of the one or more recommended actions, and outputs a recommended action with a highest expected value for the user in response to the request for the service.

Systems and methods for generating predictive data models using large data sets to provide personalized action recommendations

Various embodiments disclose an in-message application facilitating the use of third-party application programming interfaces (APIs). In particular, these in-message applications (referred to as 'cards') may be designed by a developer to access third-party API functionality. A user may then create a message referencing, e.g., a web address associated with the developer's card. The system may receive the user's message and recognize the reference. The system may then distribute an instantiation of the card to various recipients, the instantiation including elements generated using, or providing the user access to, the third party functionality.

Generating messages having in-message applications

In-message applications in a messaging platform

Growing community of inventors

San Francisco, CA, United States of America

Zachary Taylor