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 Pasteroh 机构：西弗吉尼亚大学公共摘要医疗保健费用的迅速增加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，预期寿命的增加导致该国老年人口的大幅增加。不断上涨的医疗保健费用和不断延长的寿命给许多家庭带来了坚韧的挑战。COVID-19大流行造成的广泛破坏加剧了这些国家的挑战和健康差距，特别是在健康排名不佳的农村社区。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步来解决。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应对这些挑战，以培养能够在协作跨学科团队中工作的专业人员，以开发和应用数据科学和人工智能（AI）技术来解决DH中的难题，包括农村地区。该项目预计将培训24名（24）资助和40名（40）未资助的硕士和博士生，他们来自不同的背景，包括工程学，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健康问题。该培训将通过开发以下内容来解决如何扩大和巩固卫生署的成功：1）有效且可转移的框架，以培训更大规模、更多元化的劳动力，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;以及2）解决DH中与DH数据的性质、显著的数据分析差距和计算问题相关的其他问题的新方法。培训的一个关键要素是特别关注卫生部有效和可扩展的培训和劳动力发展所需的“桥梁”。这些桥梁将连接：（a）健康科学内的不同范畴，以及其他范畴之间的不同范畴，包括不同范畴所使用的不同术语所构成的障碍;（B）透过整合不同数据类别，提供不同规模的生物医学研究（由微观至宏观）;及（c）卫生署内代表性不足的群体及创新研究，以确保受训人员的多元化。这种多样性将通过以下方式实现：1）招募和支持来自历史上的黑人学院和大学的参与者的成功，那些农村人，那些来自家庭中第一代上大学的人，和/或那些来自STEM服务不足的其他群体的人，以及2）通过桥接Priestival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）。受训者将把这些方法应用于具体的健康问题，如超声心动图的心肌指纹和基因组学中的大规模功能注释。该项目使用创新的组件序列来交织研究主题、重要的专业技能（例如，沟通，道德，领导力，协作），和课程到实习模式，而不延长时间学位。完成课程后，学员将获颁卫生署证书。实习也将形成一个新的，跨学科的博士课程在卫生署在西弗吉尼亚大学的基础。NSF研究培训（NRT）计划旨在鼓励为STEM研究生教育培训开发和实施大胆的，新的潜在变革模式。该计划致力于通过创新，循证，并与不断变化的劳动力和研究需求保持一致的综合培训模式，在高优先级的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计划和刺激竞争研究的既定计划（EPSCoR）共同资助。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。

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.