NRT-HDR: Bridges in Digital Health

提案ID: 2125872提案标题：NRT-HDR：数字健康的桥梁pi: Donald Adjeroh机构：西弗吉尼亚大学公共摘要快速增长的医疗保健成本是美国和世界上大多数其他国家面临的最紧迫的问题之一。与此同时，预期寿命的延长导致了全国老年人口的显著扩大。医疗费用的上涨和寿命的延长给许多家庭带来了严峻的挑战。COVID-19大流行造成的广泛破坏加剧了这些国家面临的挑战和卫生差距，特别是在卫生排名较差的农村社区。这些问题可以通过数字健康（DH）的新进展以及我们如何培训下一代科学家、工程师和医疗保健专业人员来开发和部署这些进展来解决。这项授予西弗吉尼亚大学（WVU）的国家科学基金会研究培训奖将通过开发一种新的研究生教育和培训模式来解决这些挑战，培养能够在跨学科团队中合作的专业人员，以开发和应用数据科学和人工智能（AI）技术来解决卫生部门（包括农村地区）的难题。该项目预计将培养24名受资助和40名未受资助的硕士和博士研究生，他们来自不同的背景，包括工程、计算机科学、医学、健康科学、物理科学和经济学。数据科学和人工智能技术已成功应用于解决各种各样的健康问题。培训将探讨如何通过发展：1)有效和可转移的框架，培训更大、更多样化的劳动力，为这些进步奠定基础，同时灌输传统课程之外的软技能，并确保为农村社区提供服务；2)解决与DH数据性质、重大数据分析差距和计算问题相关的DH其他问题的新方法。实习计划的一个关键因素是特别关注卫生署有效和可扩展的实习计划和劳动力发展所需的“桥梁”。这些桥梁将连接：(a)卫生科学的不同领域和其他领域，包括不同领域使用的不同术语造成的障碍；(b)通过整合不同类型的数据，实现生物医学研究的不同尺度（从微观到宏观）；以及(c)卫生署内代表性不足的团体和创新研究，以确保实习生的多样性。这种多样性将通过以下方式实现：1)招募和支持来自传统黑人学院和大学的参与者，那些来自农村的人，他们家庭中第一代上大学的人，和/或来自STEM服务不足的其他群体的参与者，以及2)通过连接主要本科院校和大型研究机构。学员将开发新的方法来解决人工智能中的一些核心问题（例如，信任和安全决策、可扩展的数据结构和基于注意力的信息集成）。学员将把这些方法应用于健康方面的具体问题，如超声心动图中的心肌指纹和基因组学中的大规模功能注释。该项目采用创新的组件序列，将研究主题、重要的专业技能（例如，沟通、道德、领导、协作）和课程作业交织到实习模式中，而不延长获得学位的时间。学员完成课程后，将获颁发卫生署颁发的证书。该实习也将成为西弗吉尼亚大学DH跨学科新博士项目的基础。美国国家科学基金会研究实习生（NRT）计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。该项目致力于通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式，在高优先级跨学科或融合研究领域对STEM研究生进行有效培训。该项目由NRT计划和促进竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，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。

Proposal ID: 2125872Proposal Title: NRT-HDR: Bridges in Digital HealthPI: Donald Adjeroh Institution: West Virginia University Public AbstractThe rapidly increasing cost of healthcare represents one of the most pressing problems facing the United States and most other countries around the world. At the same time, increased life expectancy has resulted in a significant expansion of the nation’s elderly population. The combination of rising health care costs and increased life spans poses tough challenges for many families. The widespread disruptions caused by the COVID-19 pandemic have exacerbated these national challenges and health disparities, particularly in rural communities with poor health rankings. These problems could be addressed by new advances in digital health (DH) and how we train the next generation of scientists, engineers, and healthcare professionals to develop and deploy such advances. This National Science Foundation Research Traineeship award to West Virginia University (WVU) will address these challenges by developing a new graduate education and traineeship model to prepare professionals who can work in collaborative transdisciplinary teams to develop and apply data science and artificial intelligence (AI) techniques in addressing difficult problems in DH, including in rural areas. The project anticipates training twenty-four (24) funded and forty (40) unfunded MS and PhD students from different backgrounds, including engineering, computer science, medicine, health sciences, physical sciences, and economics.Data science and AI techniques have been successfully applied to address a diverse range of health problems. The traineeship will address how to scale and build on DH successes by developing: 1) effective and transferable frameworks for training a larger and more diverse workforce with the foundations underlying these advances while inculcating soft skills beyond traditional coursework and ensuring rural communities are served; and 2) new ways to address other problems in DH related to the nature of DH data, the significant data analysis gap, and computational problems. A key element in the traineeship is its specific attention to the “bridges” required for effective and scalable traineeship and workforce development in DH. These bridges will connect: (a) different fields within health sciences, and across other areas, including barriers posed by the distinct terminologies used by different fields; (b) different scales of study in biomedicine (from micro to macro) via integration of different data types; and (c) underrepresented groups and innovative research in DH to ensure diversity in the traineeship. This diversity will be achieved by: 1) recruiting and supporting success for participants from Historically Black Colleges and Universities, those who are rural, those from the first generation in their families to go to college, and/or those from other groups underserved in STEM, and 2) by bridging Primarily Undergraduate Institutions and large research institutions. Trainees will develop novel approaches to tackle some of the core problems in AI (e.g., trust and safe decision making, scalable data structures, and attention-based information integration). Trainees will apply these approaches to specific problems in health, such as myocardial fingerprinting from echocardiograms and large-scale functional annotation in genomics. The project uses an innovative sequence of components to interweave the research theme, important professional skills (e.g., communication, ethics, leadership, collaboration), and coursework into the traineeship model without extending time-to-degree. Upon completion, trainees will be awarded a Certificate in DH. The traineeship will also form a basis for a new, transdisciplinary PhD program in DH at WVU. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. This project is jointly funded by the NRT program and the Established Program to Stimulate Competitive Research (EPSCoR).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.