Abstract: The system and method for automated personalized and community-specific eating and activity planning, linked to tracking with automated multimodal item identification and size estimation, enables and integrates health and other user datastreams, enables rewards and links to healthy eating and activity partners based on that data—both external and internal. The system and method also provide personalized wellness recommendations for eating, physical activity, sleep, stress reduction and other elements of daily living, tailored to each user based on the preferences, prior history, location and budget information provided by the users.

Abstract: The system and method for automated personalized and community-specific eating and activity planning are provided that are linked to tracking with automated multimodal item identification and size estimation and enable and integrate health and other user datastreams and enables rewards and links to healthy eating and activity partners based on that data. The system and method also provide personalized wellness recommendations. The system and method also enables action, such as single click ordering of the healthy meals or shopping list on one's plan from local restaurants and grocery stores, and receipt of mobile vouchers and coupons with a unique validation system for use at retailers.

Abstract: A passive nutrition tracking service automatically tracks food consumption over time for a user and for populations from a variety of food consumption sources, including food purchase input such as grocery loyalty shopping card data, restaurant dining data, estimates of food consumption within a household, estimates of free food intake and estimates of food pantry use. The tracking service also passively tracks and uses total energy expenditure information for the user, which describes the physical activity level, muscle and fat gain, and optionally waste output of the user. The tracking service analyzes the food consumption and user energy expenditure information and generates a food dietary history for the user, which includes calories and nutrient consumption estimated based on the analysis. The tracking service also generates a variety of personalized healthy lifestyle recommendations based on the analysis and presents the dietary history and personalized healthy lifestyle recommendations to the user.

Abstract: The system and method for automated personalized and community-specific eating and activity planning are provided that are linked to tracking with automated multimodal item identification and size estimation and enable and integrate health and other user datastreams and enables rewards and links to healthy eating and activity partners based on that data. The system and method also provide personalized wellness recommendations. The system and method also enables action, such as single click ordering of the healthy meals or shopping list on one's plan from local restaurants and grocery stores, and receipt of mobile vouchers and coupons with a unique validation system for use at retailers.

Abstract: A computer-implemented method is provided for analyzing patient risk and determining an individual therapeutic treatment plan for a cancer patient. The method includes entering patient medical information into a risk assessment tool; identifying any obtaining missing or out-of-date patient information; initializing the risk assessment tool based on the patient's demographics and cancer characteristics and determining a default treatment plan for the patient; modifying the default treatment plan by observing the modification of a risk score for the patient; and confirming a treatment order for the patient based on a balancing of the risk and treatment plan factors.

Abstract: Methods, systems, and computer program products for developing and using predictive models for predicting medical outcomes and for evaluating intervention strategies, and for simultaneously validating biomarker causality are disclosed. According to one method, clinical data from different sources for a population of individuals is obtained. The clinical data may include different physical and demographic factors regarding the individuals and a plurality of different outcomes for the individuals. Input regarding a search space including models linking different combinations of the factors and at least one of the outcomes is received. In response to receiving the input, a search for models in the search space based on predictive value of the models with regard to the outcome is performed. The identified models are processed to produce a final model linking one of the combinations of factors to the outcome.