R33AG068931

Administrative Supplement to Support Collaborations to Improve AI/ML-Readiness - Summary

This application is for an administrative supplement (revision) to an existing award, R33AG068931, titled "Advanced Development and Utilization of Assembled Aging Trajectory Files from Multiple Datasets." The goal of the parent study is to create a comprehensive research repository of aging trajectory datasets and to demonstrate their utility for aging research at Rutgers University through four specific aims.

1) Harmonizing and merging multiple datasets to generate the data infrastructure needed to understand change over time in care settings, geriatric syndromes, physical functioning, and shared risk factors at multiple levels and across multiple domains.
2) Developing state-of-the-art analytic methods to identify patterns of aging trajectories experienced by older adults during the final years of life and their association with shared risk factors and distal outcomes.
3) Discovering multilevel and potentially interactive predictors of trajectories using both model-based approaches and machine learning algorithms to predict specific outcomes.
4) Disseminating resources generated, including datasets, documentation, source code, and methodology.

For the supplement, new work in the CMS Virtual Research Data Center (VRDC) will create AI/ML-ready datasets, workflows, and source code for data cleaning and pre-processing, breaking the siloed barriers between researchers working in the VRDC and institutional data enclaves at universities. Data harmonization procedures need to be customized to the server architecture and resources of each data warehouse, necessitating VRDC-specific workflows and code to ensure timely access and reproducibility.

In this project, data are made AI/ML-ready in four stages:
1) The cohort of patients to be studied is defined, and key inclusion and exclusion criteria variables are selected.
2) Data pre-processing steps include data cleaning, data annotation, formatting, standardizing taxonomies, variables transformation, data rescale/normalization, variable aggregating, variable decomposing, and variable selection with a focus on variables important to measure health disparities and improve minority health and reduce health disparities.
3) Feature extraction and engineering include generating derived variables (e.g., intercept, slope, average, etc.) from irregularly spaced individual trajectories.
4) Medicare datasets are merged with publicly available data to add socioeconomic and environmental context, and data variable relationships are mapped to produce a final, AI/ML-ready data.

Supplement Aim: Develop and implement code for data pre-processing, data fine-tuning and precision, missing data imputation, data connectivity, and fully established hierarchical relationships for the AI/ML framework to interactively model late-life aging trajectories and selected outcomes in a cohort of Medicare beneficiaries.

Completion of this work will contribute to the NIH vision of a modernized and integrated biomedical data ecosystem that adopts the latest data science technologies and best practice guidelines, including FAIR (Findable, Accessible, Interoperable, Reusable) principles and open-source development.

Rutgers The State University Of New Jersey

TO ENCOURAGE BIOMEDICAL, SOCIAL, AND BEHAVIORAL RESEARCH AND RESEARCH TRAINING DIRECTED TOWARD GREATER UNDERSTANDING OF THE AGING PROCESS AND THE DISEASES, SPECIAL PROBLEMS, AND NEEDS OF PEOPLE AS THEY AGE. THE NATIONAL INSTITUTE ON AGING HAS ESTABLISHED PROGRAMS TO PURSUE THESE GOALS. THE DIVISION OF AGING BIOLOGY EMPHASIZES UNDERSTANDING THE BASIC BIOLOGICAL PROCESSES OF AGING. THE DIVISION OF GERIATRICS AND CLINICAL GERONTOLOGY SUPPORTS RESEARCH TO IMPROVE THE ABILITIES OF HEALTH CARE PRACTITIONERS TO RESPOND TO THE DISEASES AND OTHER CLINICAL PROBLEMS OF OLDER PEOPLE. THE DIVISION OF BEHAVIORAL AND SOCIAL RESEARCH SUPPORTS RESEARCH THAT WILL LEAD TO GREATER UNDERSTANDING OF THE SOCIAL, CULTURAL, ECONOMIC AND PSYCHOLOGICAL FACTORS THAT AFFECT BOTH THE PROCESS OF GROWING OLD AND THE PLACE OF OLDER PEOPLE IN SOCIETY. THE DIVISION OF NEUROSCIENCE FOSTERS RESEARCH CONCERNED WITH THE AGE-RELATED CHANGES IN THE NERVOUS SYSTEM AS WELL AS THE RELATED SENSORY, PERCEPTUAL, AND COGNITIVE PROCESSES ASSOCIATED WITH AGING AND HAS A SPECIAL EMPHASIS ON ALZHEIMER'S DISEASE. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO EXPAND AND IMPROVE THE SBIR PROGRAM, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.

93.866 - Aging Research

National Institute on Aging (NIA) [HHS - NIH]

Newark, New Jersey 071073001 United States

Single Zip Code

Advanced-Stage Development and Utilization of Research Infrastructure for Interdisciplinary Aging Studies (R33 Clinical Trial Optional) (PAR-20-071)

Amendment Since initial award the End Date has been extended from 04/30/24 to 08/31/26 and the total obligations have increased 66% from $2,319,910 to $3,849,978.