Call for Applications (Closed)

AIM-AHEAD Bridge2AI for Clinical Care

AIM-AHEAD Traineeship in Advanced Data Analysis using the Bridge2AI AI/ML for Clinical Care Network

Program Description

The application of artificial intelligence and machine learning (AI/ML) to large datasets is dramatically expanding the capacity for hypothesis testing, impacting the biomedical and socio-economic domains. However, underrepresented communities, particularly those at heightened risk of socioeconomic and health disparities, are not receiving AI/ML’s benefits. There is an urgent need to promote equitable competencies development and capacity by enabling access to and skills for utilizing high-resolution and standardized data and software building for growing and diversifying the next-generation AI scholars. Training a diverse workforce of data science researchers proficient in applying AI/ML to clinical care represents an opportunity to address a critical unmet need by extending the benefits of AI/ML to underrepresented, low-resourced communities. 

The central goal of the AIM-AHEAD Bridge2AI for Clinical Care Training Program is to expand Bridge2AI AI/ML for Clinical Care data access through engagement, training, and mentorship, including the use of AI/ML in big data analysis for underrepresented trainees. This effort will allow AIM-AHEAD fellows to conduct novel data-driven research at the intersection of AI/ML and health disparities with a multi-modal array of data elements from a diverse cohort. 

To accomplish this objective, diverse professionals committed to applying AI/ML to benefit underrepresented communities will complete an intensive 8-month program in advanced statistical and computational data analysis developed by the Bridge2AI team, utilizing the resources of the Bridge2AI AI/ML for Clinical Care Network and AIM-AHEAD’s Data Science Training Core. Completing the training will equip the motivated professional to conduct the in-depth analysis of large datasets essential for cutting-edge biomedical research.

Program Objectives

An important secondary outcome is that these activities will result in early trainee feedback regarding the Bridge2AI AI/ML for Clinical Care Collaborative Cloud platform environment, enabling immediate iterative improvement to maximize its wider trainee utilization and integration with other Common Fund resources.

The AIM-AHEAD consortium represented by the Data Science Training Core and Communications Hub, and Bridge2AI, including the AI/ML for Clinical Care (CHoRUS Program) and Bridge Center, are partnering to offer a data science training program to allow for further development of a diverse workforce of researchers who are proficient in AI/ML and eager to address unmet needs in underrepresented communities. This program is aimed to significantly accelerate the uptake of AI in Clinical Care through leveraging the Bridge2AI AI/ML for Clinical Care data, curriculum, tools, and train-the-trainer activities with the AIM-AHEAD expertise in trainee recruitment and development.

The AI/ML for Clinical Care Network, part of the Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI, endeavors to offer a diverse, high-resolution, ethically sourced, AI-ready data set to respond to the challenge of improving recovery from acute illness (https://bridge2ai.org/chorus/). Drawing from 14 academic institutions, this dataset is unique in its inclusion of high-resolution waveforms, and trended EHR data of high resolution with deliberate incorporation of Social Determinants of Health (SEDoH) data from geomapping, with imaging data being incorporated over time. The collaborative cloud platform enables users to log in and access the dataset, apps, and tools for interpretable AI. Including SDOH data ensures that healthcare research outcomes reflect the complex societal factors that influence health disparities, providing a holistic view of patient health.

The collection and curation of data within the Bridge2AI AI/ML for Clinical Care Networks are conducted cautiously, ensuring that individual privacy is safeguarded while simultaneously capturing the complex factors that impact health outcomes. In parallel, the network has developed a series of microlearning curricula, AI/ML for Clinical Care Workshops, including hands-on modules for advanced and beginner trainees, and a mentorship network supported by train-the-trainer activities. 

Using the AIM-AHEAD Connect Platform, this 8-month training program will engage a diverse group of up to 25 graduate students, postdocs, early-career faculty, healthcare professionals, and other non-academic professionals from underrepresented populations. Trainees will receive hands-on training on the Bridge2AI AI/ML for Clinical Care Network and leverage the data and tools to create practical use cases, putting their new skills to work in real-life situations and innovative data-driven research. 

Training Integration and Resource Allocation will include:

• • Virtual live courses and workshops focused on introductory machine learning and feature engineering
  • Hands-on training on the Bridge2AI AI/ML for Clinical Care Collaborative Cloud
  • Workshops using AI/ML for Clinical Care canonical Jupyter Notebooks
  • Introductory workshops on the OHDSI/OMOP common data model

Training Implementation, Use Case Development, and Evaluation will include:

• • Workshops and office hours on using the OHDSI tool stack for trainee projects
  • Didactics on generative AI and specific use cases to progress trainee projects
  • Trainee creation of practical use cases during Bridge2AI topics, putting new skills to work in real-life situations
  • Ongoing mentorship and support through the AIM-AHEAD Connect and Bridge2AI AI/ML for Clinical Care (CHoRUS) Collaborate Cloud platforms

This robust educational framework empowers the next generation of healthcare professionals with the tools and knowledge necessary to drive the field of AI in precision clinical care focused on underrepresented communities forward. Having received advanced practical training in AI/ML Cloud Computing, AI/ML Notebooks, AI/ML Principles, and Knowledge of AI-Ready Standards, trainees completing this program will be well-prepared to harness AI/ML approaches to conduct hypothesis-driven analysis of diverse complex datasets. The trainees will join the community of AI/ML professionals passionately committed to extending the benefits of AI/ML to communities underrepresented in biomedical research. After completing the program, trainees will also have access to post-training opportunities, such as continued mentorship, collaboration within the Bridge2AI network, and ongoing research resources.

-- Applications are due by Monday, November 18, 2024, at 11:59 p.m. Eastern Time. 

-- Training through AIM-AHEAD Connect will begin on January 15, 2025. 

Trainee Expectations

• • Attend all training sessions, both synchronous and asynchronous, including workshops, webinars and seminars
  • Work on the program an average of at least 8 hours per week
  • Engage with an AIM-AHEAD Mentor (to be assigned through the program)
  • Engage in learning communities and peer networking
  • Access the Bridge2AI AI/ML for Clinical Care (CHoRUS) data
  • Complete the provided training related to R, Python, and Jupyter Notebook, available via AIM-AHEAD Connect
  • Complete the onboarding process including an Orientation
  • Utilize Office Hours and Concierge Services provided by Tufts Team
  • Utilize AIM-AHEAD HelpDesk support 
  • Present a work-in-progress research poster at a Bridge2AI Meeting in spring 2025 and the AIM-AHEAD Annual Meeting in summer 2025
  • Generate an abstract suitable for submission to a conference, and/or a manuscript suitable for peer-reviewed publication
  • Play an active part in the AIM-AHEAD community

Trainees Will Receive

• • A stipend totaling $8,000, plus a $2,000 allowance to attend the AIM-AHEAD and Bridge2AI Annual Meetings in 2025
  • Support and guidance from an experienced AIM-AHEAD mentor
  • Support from the AIM-AHEAD Data Science Training Core
  • Direct 1:1 guidance, virtual office hours, HelpDesk support and concierge services supporting R and Python coding and the OHDSI tool stack
  • Training on:
    • Introductory machine learning and feature engineering
    • The Bridge2AI AI/ML for Clinical Care Collaborative Cloud
    • Ethics and Policy issues in AI/ML
    • AI/ML for Clinical Care canonical Jupyter Notebooks
    • The OHDSI/OMOP common data model
    • Generative AI and specific use cases
    • Creating practical use cases during Bridge2AI topics

Traineeship Stipend

Each trainee will receive a stipend of $8,000 which will be disbursed in four installments based on trainee completion of required milestones. Each trainee will also be provided an allowance of $2,000 to cover the cost of airfare, hotel accommodations, local transportation and per diem to attend Annual Meetings for AIM-AHEAD and Bridge2AI in 2025.* 

*Trainees may apply to multiple AIM-AHEAD training programs and fellowships, but if accepted into more than one, they must select only one AIM-AHEAD training program or fellowship to participate in per funding cycle. Additionally, AIM-AHEAD-funded Coordinating Center Personnel or AIM-AHEAD-funded Awardees (MPIs, PIs, Co-Investigators, other personnel) are eligible to apply, but if accepted, they will not receive the stipend and allowance.*

Trainee Mentorship

Each awarded trainee will receive mentorship from experienced, skilled investigators selected from AIM-AHEAD core members, who will guide the trainee in developing testable hypotheses using AI/ML for Clinical Care data. The online mentoring platform AIM-AHEAD Connect (https://connect.aim-ahead.net) will be used to match mentors with awarded trainees and for mentor/trainee engagement and progress tracking.

1. Applicants must be:
   1. U.S. Citizens, Permanent Residents, or Non-Citizen U.S. Nationals
      • U.S. Citizen: An individual who is a citizen of the United States by law, birth or naturalization (https://www.law.cornell.edu/definitions/uscode.php?width=840&height=800&iframe=true&def_id=42-USC-630966247-802284531&term_occur=1&term_src=title:42:chapter:99:section:9102)
      • Permanent Resident: An immigrant/non-citizen who can legally reside in the United States in perpetuity (https://www.law.cornell.edu/wex/lawful_permanent_resident_(lpr))
      • Non-Citizen National: A person born in an outlying possession of the United States on or after the date of formal acquisition by the United States (https://www.law.cornell.edu/uscode/text/8/1408)
   2. Able to submit Form W-9 (Request for Taxpayer Identification)
   3. Affiliated with one of the following entities:
      • Higher Education Institutions
        • Public/State Controlled Institutions of Higher Education8
        • Private Institutions of Higher Education

        Individuals affiliated with the following types of Higher Education Institutions are always encouraged to apply:

        • Hispanic-serving Institution
        • Historically Black Colleges and Universities (HBCUs)
        • Tribally Controlled Colleges and Universities (TCCUs)
        • Alaska Native and Native Hawaiian Serving Institutions
        • Asian American Native American Pacific Islander Serving Institutions (AANAPISIs)
      • Nonprofits Other Than Institutions of Higher Education
        • Nonprofits with 501(c)(3) IRS Status (Other than Institutions of Higher Education)
        • Nonprofits without 501(c)(3) IRS Status (Other than Institutions of Higher Education)
      • For-Profit Organizations
        • Small Businesses
        • For-Profit Organizations (Other than Small Businesses)
2. Education: Applicants can be post-baccalaureate or graduate students, postdoctoral fellows, medical students or residents, allied health trainees, early-career investigators, or early-career employees of non-academic institutions as defined in item 1C above. Applicants must hold at a minimum a bachelor’s degree from an accredited U.S. institution in one of the following or related fields:
   • • Physical sciences (e.g., physics)
     • Biological or life sciences (e.g. biology, biochemistry, microbiology)
     • Mathematics or statistics
     • Data science and machine learning
     • Electrical or Biomedical Engineering
     • Health sciences (e.g. pharmacy, psychology, health information technology, nurses, therapists, social workers)
     • Public health (epidemiology, biostatistics, health administration, clinical implementation specialists)
3. Recommended Applicant Knowledge, Skills, and Experience:

   To ensure success in the training program, applicants must already possess certain skills, knowledge, and experience These include:

   • Has practical experience in coding/programming with R or Python
   • Basic understanding of statistics
   • A working command of English, as all training will be conducted in English

   Additionally, it is strongly recommended that applicants have some of the following skills and experiences to optimize their learning experience and better prepare them for the challenges of the program.

   • Successfully completed an undergraduate or graduate course in probability and statistics
   • Has practical experience in coding/programming with R or Python
   • Has experience in data manipulation and management gained through coursework and/or research projects

   Introductory or refresher courses on these topics will be available to successful applicants at the start of the traineeship, via the AIM-AHEAD Connect platform.

Applications are now closed.

A review committee comprised of AIM-AHEAD Consortium members will apply the following criteria to evaluate and prioritize applications. In assigning priority scores, reviewers will apply the standard NIH 1-9 scoring range to Criteria 1, where a score of 1 indicates highest enthusiasm, and a score of 9 indicates lowest enthusiasm, based on NIH Simplified Review Framework - https://grants.nih.gov/grants/guide/notice-files/NOT-OD-24-010.html

Criteria 1: 

1. 1. Articulation of Expectations and Reasons for Participation: Evaluate the clarity and depth with which the applicant articulates personal expectations and motivations for joining the program. Consider how convincingly the applicant communicates the necessity and significance of the training for their career or academic ambitions.
   2. Research Background and Motivation for Training: Assess the extent of the applicant’s relevant background, professional experience, or academic qualifications that support their readiness for this program. Gauge their motivation and potential to actively participate in and derive meaningful benefits from the training.
   3. Long-term Application of Training: Examine the specificity and feasibility of the applicant’s plans to apply AI/ML training in their research or professional development. Look for detailed strategies that indicate a commitment to integrating the training into their long-term career or academic objectives.
   4. Support from Supervisors or Mentors: Determine if the letter of support provides strong and unequivocal commitment from the supervisor, faculty, or mentor. It should confirm the provision of sufficient protected time for the applicant to fully engage with the training.
   5. Reference(s) and Assurance of Success: Critique the letter(s) of reference for their effectiveness in providing a persuasive argument that the applicant is well-prepared and likely to succeed in the program. The reference(s) should highlight relevant skills, accomplishments, and the applicant's capacity for advanced training.
   6. Community Engagement and Collaboration: Evaluate how well the applicant expresses a readiness to actively engage with the AIM-AHEAD community. Look for a demonstrated commitment to contributing to communal resources, empowering new users, and promoting a culture of diversity and inclusivity within the community.

Criteria 2:

Prior Data Science Experience: Assess applicant data science experience by reviewing education, work history, programming proficiency, project involvement, and understanding of math/statistics applied in data analysis. 

To be evaluated by selecting one of the following options from a drop-down menu.

• • Beginner
  • Intermediate 
  • Expert

Applicants should expect notification of their acceptance status by Monday, January 6, 2025. Accepted applicants will receive an invite from PaymentWorks requesting:

• a valid tax ID (either an EIN or SSN) via W9 for U.S. vendors
• to upload a Bank Validation file for ACH/EFT or Wire Payments (https://community.paymentworks.com/payees/s/article/What-Is-A-Bank-Validation-File)

If an applicant applies and is accepted into more than one AIM-AHEAD training program, they must select only one to participate in per funding cycle. Additionally, AIM-AHEAD-funded Coordinating Center Personnel or AIM-AHEAD-funded Awardees (MPIs, PIs, Co-Investigators, other personnel) are eligible to apply, but if accepted, they will not receive the stipend and allowance.

There was an informational webinar on Wednesday, November 6, 2024

Watch the webinar recording here

Open the webinar slides here

AIM-AHEAD Bridge2AI for Clinical Care Training Program Co-Directors

Eric Rosenthal, MD, Nawar Shara, PhD, Toufeeq Syed, PhD

Frequently Asked Questions

Please refer to the FAQs below before submitting a help desk ticket:

AIM-AHEAD Bridge2AI for Clinical Care Frequently Asked Questions

Please feel free to submit a help desk ticket if you have any questions:

AIM-AHEAD Training Programs HelpDesk