Abstract: A computing system can implement a health verification and prediction service by generating an interactive user interface through which users can provide responses to health assertions in one or more trivia sessions. Each of the health assertions can include a correlation value to actual health outcomes based on responses received from individuals a control group and the known health outcomes of those individuals. Based on the responses from the user, the computing system can generate a health profile for the user.

Abstract: A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a morbidity risk profile that is used to classify the user in an underwriting class.

Abstract: A computing system includes a database having a collection of health-related assertions for a health quiz to be taken by users. Each health assertion is tuned with a set of correlative health values based on responses to the health assertion provided by a control group of users. The system can determine the set of correlative health values for each health assertion based on the actual health outcomes of the control group users. Using the correlative values for each health assertion and activity data from a wearable activity monitoring device of a user, the computing system can generate a health profile of the user who interacts with a health trivia session comprising a subset of the health assertions.

Abstract: A computing system can execute a correlation model to determine correlation values for health assertions, where the correlation values for each health assertion indicates correlations between the health assertion and one or more actual health outcomes. Actual health outcomes are provided by control group users who have also provided responses to each of the health assertions. The computing system may then provide users with a health trivia session comprised of these health assertions, and based at least in part on the performance of the user during the trivia session, generate a health profile for the user that is predictive of one or more health outcomes.

Abstract: A method of operating a model-based machine-learning data digest system comprises acquiring and storing data input at one quality level transforming the input into a first transform output usable by a machine learning component; performing a first test iteration of the machine learning component on the first transform output; retrieving the first quality level saved data; modifying the retrieved copy to a second quality level; transforming the retrieved copy into a second transform output usable by a machine learning component; performing a second test iteration on the second transform output; comparing validity measures of the first and the second test output; and if a validity measure of the second test output is equal to or greater than a validity measure of the first test output, instructing the data source to provide at least one future instance of data input at the second data quality level.

Abstract: A computer-implemented method of operation of a model-based machine learning data digest system comprises acquiring a data input originating at a data source; transforming the data input through at least one intermediate data state into a transform output in a form usable by a model-based machine learning component; monitoring a flow of data transformation operations that perform the transforming of the data input through at least one intermediate data state into a transform output; annotating the transform output with an annotation comprising metadata derived from the monitoring; and adjusting, according to the annotation, at least one control parameter operable to control at least one operation of the flow of data transformation operations that perform the transforming of the data input through at least one intermediate data state into the transform output.

Abstract: A model-based machine learning data digest system comprises acquiring a data input originating at a data source; first monitoring the acquiring of the data input; transforming the data input through at least one intermediate data state into a transform output in a form usable by a model-based machine-learning component; second monitoring a flow of data transformation operations that perform the transforming of the data input through at least one intermediate data state into a transform output; third monitoring at least one iteration of operation of the model-based machine-learning component on the transform output; deriving from the first, second and third monitoring a modified data input specification to increase at least one measure of flow performance from data acquisition to completion of the iteration of operation of the model-based machine-learning component; and deploying the modified data input specification to at least one data source.

Abstract: A computing system can execute a correlation model to determine correlation values for health assertions, where the correlation value for each health assertion indicates a level of correlation between the health assertion and one or more actual health outcomes. Actual health outcomes are provided by control group users who have also provided responses, either correct or incorrect, to each of the health assertions. The computing system may then provide users with a health trivia session comprised of these health assertions, and based at least in part on the performance of the user during the trivia session, generate a health profile for the user.

Abstract: A computing system includes a database having a collection of health-related assertions for a health quiz to be taken by users. Each health assertion is tuned with a correlative health value based on answers to the health assertion provided by a control group of users. For each user in the control group, at least one health outcome is known, and therefore the computing system can use a big data approach to determine the correlative value for each health assertion to the actual health outcomes of the control group users. Using the correlative values for each health assertion, the computing system can generate a health profile of each user who takes the health quiz. In certain examples, the health quiz correlation can be combined with activity monitoring data to develop a highly accurate health profile for each user.

Abstract: A computing system can implement a health verification and prediction service by generating an interactive user interface through which users can provide responses to health assertions in one or more trivia sessions. Each of the health assertions can include a correlation value to actual health outcomes based on responses received from individuals a control group and the known health outcomes of those individuals. Based on the responses from the user, the computing system can generate a health profile for the user.

Abstract: A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a morbidity risk profile that is used to classify the user in an underwriting class.

Abstract: A computing system can implement a health verification and prediction service by generating an interactive user interface through which users can provide responses to health assertions in one or more trivia sessions. Each of the health assertions can include a correlation value to actual health outcomes based on responses received from individuals a control group and the known health outcomes of those individuals. Based on the responses from the user, the computing system can generate a health profile for the user.

Abstract: A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a mortality outcome profile that is used to classify the user in an underwriting class.

Abstract: A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a mortality outcome profile that is used to classify the user in an underwriting class.

Abstract: A computing system can execute a correlation model to determine correlation values for health assertions, where the correlation value for each health assertion indicates a level of correlation between the health assertion and one or more actual health outcomes. Actual health outcomes are provided by control group users who have also provided responses, either correct or incorrect, to each of the health assertions. The computing system may then provide users with a health trivia session comprised of these health assertions, and based at least in part on the performance of the user during the trivia session, generate a health profile for the user.

Abstract: A computing system includes a database having a collection of health-related assertions for a health quiz to be taken by users. Each health assertion is tuned with a correlative health value based on answers to the health assertion provided by a control group of users. For each user in the control group, at least one health outcome is known, and therefore the computing system can use a big data approach to determine the correlative value for each health assertion to the actual health outcomes of the control group users. Using the correlative values for each health assertion, the computing system can generate a health profile of each user who takes the health quiz. In certain examples, the health quiz correlation can be combined with activity monitoring data to develop a highly accurate health profile for each user.

Abstract: A system and method for predicting a health outcome of a user based on a determination of knowledge the user possesses regarding issues of physiological or mental health.

Abstract: Examples described herein include a computer system to obtain social network content from a user, and to analyze the social network content to determine one or more attributes. The computer system predicts a lifestyle category of the user based at least in part on the determined one or more attributes.

Abstract: A computer system automates the determination of a health profile of a user based on a knowledge of the user regarding health related questions and social network content from an account of the user.

Abstract: Examples are provided herein for providing a service in connection with determining a health of a user. A collection of questions are stored for the user, with each question being based on a documented assertion pertaining to human health. Each question in the collection of questions is associated with a correlative health parameter that is based at least in part on (i) persons in a control population that have independent knowledge of an assertion that is a basis of that question, and (ii) a value of a predetermined health parameter for each person in the control population, the value of the predetermined health parameter for each person being indicative of that person's health. Based on data generated by one or more activity monitoring devices, questions from the collection are selected for display to the user.