The mounting need to improve the quality and efficiency of healthcare due to economic pressures and changing demographics requires significant transformations in healthcare delivery. This new healthcare that will be evidence-based, patient-centered and proactive (preventive) is likely to depend in part on technological innovations that in turn require solutions to fundamental scientific and engineering problems. In response to these challenges, NSF has developed a program in smart health and wellbeing that is focused on stimulating relevant research in key areas, including computer science, engineering, and behavioral and social sciences, reflecting the multidisciplinary nature of problems. In this presentation I will describe the current program as well as a number of challenges ranging from the development and utilization of inexpensive monitoring technologies, e.g. mobile health, to the development of sophisticated inference algorithms and economically feasible intervention schemes. I will emphasize the increasing need to understand the relationship between behaviors and health, the notion of behavioral phenotyping, and the corresponding challenges involving the development of behavioral markers. The success of these efforts will greatly benefit from the development and application of computational predictive modeling at multiple scales, ranging from molecular biology to behavioral data and social networks. Among the key issues requiring new research efforts, I will identify the need for the development of patient-specific computational multi-scale models that will enable the utilization of heterogeneous, (big) data in conjunction with individual-specific observations and measurements. I will illustrate the model-based approaches on a small sample of specific examples.
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