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

合作研究：PPoSS：规划：全国范围内个性化精准医疗的边缘传感和计算原则

• 批准号: 2028888
• 负责人: Song Han
• 金额: $ 10万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028888&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Principles

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.

利用边缘传感计算的个性化精准医疗保健（PPH）可以收集，分析和解释连续的多模态数据，包括身体和生理数据，从而产生在个人和人群水平上实时监测疾病发作和进展所需的信息，知识和见解。该规划提案将（i）确定挑战并调查边缘传感计算范式的原则和潜在解决方案;（ii）吸引不同的学术，社区和政府利益相关者共同定义PPH的功能和性能要求;（iii）创建和验证初步方法，并制定将PPH扩展到国家层面的具体详细计划。这与NSF的使命“促进国家健康、繁荣和福利”是一致的。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（例如疾病爆发/蔓延、急性/传染病的早期发现/预防性干预）和管理慢性生理和心理状况。PI将1）在学术，工业和社区场所传播出版物，数据和系统; 2）整合CISE学生教育（包括女性和代表性不足的少数民族）; 3）指导高中学生进行联合卫生技术研究; 4）培养技术素养的医疗保健工作队伍; 5）试点技术，使附近社区立即受益，同时研究如何扩展到其他农村，郊区和城市环境。该项目将探索，设计，并在不同类型的传感数据、分析算法、疾病、健康状况和人口规模的四个维度上评估增强基于边缘传感计算的PPH可扩展性的潜在解决方案。PI将识别挑战并验证以三项原则为指导的方法：作为一等公民的隐私、针对错误的设计和规模利用。该小组将：1）为跨硬件、软件和应用堆栈的端到端安全性和隐私保证定义新的抽象和可量化的度量; 2）研究用于异构医疗保健数据的多时间分辨率处理的系统，包括来自可能不受信任的第三方的组件的组合，并适应噪声、干扰甚至是对手控制的数据; 3）探索适合PPH学习和推理的新型AI/机器学习算法，包括用于神经网络架构搜索的AutoML，模型压缩和极端规模的联邦学习，同时满足安全性，隐私性和鲁棒性约束;和4）开发异构硬件加速器以及用于神经硬件架构协同设计、时间序列、点云和语言/声音理解的高效加速的通用设计方法和工具，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。