Collaborative Research: PPoSS: Planning: Principles for Edge Sensing and Computing for Personalized, Precision Healthcare at National Scale

利用边缘传感计算的个性化、精确医疗保健(PPH)可以收集、分析和解释连续的多模式数据，包括物理和生理数据，产生在个人和人群水平上实时监测疾病发病和进展所需的信息、知识和洞察力。这项规划提案将(I)确定挑战，并调查边缘感知-计算范例的原则和潜在解决方案；(Ii)动员不同的学术、社区和政府利益攸关方共同界定PPH的功能和性能要求；以及(Iii)创建和验证初步方法，并制定具体、详细的计划，将PPH推广到国家层面。这与美国国家科学基金会“促进国民健康、繁荣和福利”的使命非常一致。该项目可以为社区、医疗保健系统和其他利益相关者带来巨大的社会和经济效益。如果成功，该项目将能够监测流行病(例如疾病的暴发/传播、急性/传染病的早期发现/先发制人的干预)以及管理慢性的身体和心理状况。PIS将1)在学术、行业和社区场所传播出版物、数据和系统；2)在不同层面整合CISE学生教育(包括女性和代表性不足的少数族裔)；3)指导高中生进行联合卫生技术研究；4)培养一支精通技术的医疗队伍；以及5)在研究如何扩展到其他农村、郊区和城市环境的同时，试点直接惠及附近社区的技术。本项目将从不同类型的传感数据、分析算法、疾病、健康状况和人口规模四个维度探索、设计和评估基于边缘感知计算的PPH的可扩展性。PIS将识别挑战并验证遵循三个原则的方法：作为一等公民的隐私、针对错误的设计和利用规模。该团队将：1)为跨硬件、软件和应用程序堆栈的端到端安全和隐私保证定义新的抽象和可量化的度量标准；2)研究用于异类医疗数据的多时间分辨率处理的系统，包括来自可能不可信的第三方的组件的组合，并适应噪声、干扰甚至对手控制的数据；3)探索适用于PPH学习和推理的新型人工智能/机器学习算法，包括用于神经网络结构搜索、模型压缩和极端规模的联合学习的AutoML，同时满足安全、隐私和健壮性限制；以及4)开发异类硬件加速器和通用设计方法和工具，用于神经-硬件架构的联合设计、时间序列的高效加速、点云和语言/声音理解以及设备边缘培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.