NRT-HDR: Bridges in Digital Health

  • Funded by National Science Foundation (NSF)
  • Total publications:13 publications

Grant number: 2125872

Grant search

Key facts

  • Disease

    COVID-19
  • Start & end year

    2021
    2026
  • Known Financial Commitments (USD)

    $3,000,000
  • Funder

    National Science Foundation (NSF)
  • Principal Investigator

    Donald Adjeroh
  • Research Location

    United States of America
  • Lead Research Institution

    West Virginia University Research Corporation
  • Research Priority Alignment

    N/A
  • Research Category

    Secondary impacts of disease, response & control measures

  • Research Subcategory

    Social impacts

  • Special Interest Tags

    Digital Health

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Adults (18 and older)

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

Proposal ID: 2125872
Proposal Title: NRT-HDR: Bridges in Digital Health
PI: Donald Adjeroh
Institution: West Virginia University

Public Abstract
The 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.

Publicationslinked via Europe PMC

Last Updated:an hour ago

View all publications at Europe PMC

Limitations and risks of custom GPTs in dermatology. Comment on "ReconGPT: A novel artificial intelligence tool and its potential use in post-Mohs reconstructive decision-making".

Preliminary evaluation of ChatGPT model iterations in emergency department diagnostics.

Synthetic generation of cardiac tissue motion from surface electrocardiograms.

Adapting ChatGPT for Color Blindness in Medical Education.

Cardiac ultrasomics for acute myocardial infarction risk stratification and prediction of all-cause mortality: a feasibility study.

Evaluation of machine learning models that predict lncRNA subcellular localization.

Molecular Signatures of CB-6644 Inhibition of the RUVBL1/2 Complex in Multiple Myeloma.

Online continual decoding of streaming EEG signal with a balanced and informative memory buffer.