EAGER: Personal Models for a Navigational Approach to Health

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Scientific and technological advances in the last century have helped humankind move past infectious disease as the primary cause of mortality. Average lifespan has doubled from 40 years to above 80 years in developed countries. Today, chronic disease presents our greatest health challenge. Current healthcare practice based on the episodic infectious disease health model is not a good approach to treating chronic disease. Instead, healthcare needs to focus on diet, exercise, and other lifestyle activities. Future health systems will use rich, multi-modal data to sense health state continuously and provide guidance in making daily lifestyle decisions. The lifestyle navigational approach is a close-loop sense-estimate-act cycle based on sensors, computing elements, and actuators and closely guided by the personal model of the individual. To provide guidance, predict the future, or understand preferences of an individual, we must build a model that is specific to an individual. The personal model contains not only ordinary medical record data, but also information about disease propensity and lifestyle behaviors.This project will develop methods to easily and accurately track behaviors that have an impact on health and chronic disease, and methods for incorporating such information into a personalized lifestyle health model. These models will be critical in developing a navigational approach to health in order to facilitate precision medicine based on predictive and preventive approaches. Nutrition is one of the major components of lifestyle, and yet there are no fully satisfactory approaches for food logging. This proposal will specifically create a novel approach to food logging based on using a wearable sensor to detect heart rate changes associated with eating, which triggers an ecological momentary assessment (EMA) for food logging. A food log compared with already available activity logs provides enough quantitative information to understand and analyze lifestyle quantitatively for a person. This project will develop novel event mining techniques by extending machine learning approaches for building lifestyle related causal models for a person that will be used in predictive approaches as well as in guiding the person's lifestyle decisions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.