Personalized, precision healthcare (PPH) utilizing edge sensing-computing can collect, analyze and interpret continuous, multi-modality data, both physical and physiologic, producing information, knowledge and insight needed for real-time disease onset and progression monitoring at both the individual and population levels. This planning proposal will (i) identify the challenges and investigate the principles and potential solutions for the edge sensing-computing paradigm; (ii) engage diverse academic, community and government stakeholders to collectively define the functional and performance requirements for PPH; and (iii) create and validate preliminary approaches and devise a concrete, detailed plan for scaling PPH to national levels. It is well aligned with NSF’s mission to “advance the national health, prosperity and welfare.†This project can generate enormous social and economic benefits for communities, healthcare systems, and other stakeholders. If successful, the project will enable the monitoring of epidemics (e.g. disease outbreaks/spread, early detection/preemptive intervention of acute/infectious diseases) and the management of chronic physical and psychological conditions. The PIs will 1) disseminate publications, data and systems in academic, industry and community venues; 2) integrate CISE student education (including female and under-represented minorities) at different levels; 3) mentor high-school students on joint health-technology research; 4) cultivate a technology-literate healthcare workforce; and 5) pilot the technologies for immediate benefits to nearby communities while studying how to scale to other rural, suburban, and city settings.
This project will explore, design, and evaluate potential solutions for enhancing the scalability of edge sensing-computing-based PPH in four dimensions of different types of sensing data, analytic algorithms, diseases, health conditions, and population sizes. The PIs will identify challenges and validate approaches guided by three principles: privacy as a first-class citizen, design for faults and exploitation of scale. The team will: 1) define new abstractions and quantifiable metrics for end-to-end security and privacy guarantees across hardware, software and application stack; 2) investigate systems for multi-temporal resolution processing of heterogeneous healthcare data, incorporating composition of components from possibly untrusted third parties and accommodate noises, disturbances or even adversary-controlled data; 3) explore novel AI/machine-learning algorithms suitable for PPH learning and inference, including AutoML for neural-network architecture search, model compression and federated learning at extreme scale while meeting security, privacy and robustness constraints; and 4) develop heterogeneous hardware accelerators and general design methodologies and tools for neural-hardware architecture co-design, efficient acceleration for time-series, point-cloud and language/sound understanding, and on-device edge training.
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.
