Published on: Oct 13, 2025
LATROBE, PA — Saint Vincent College has been awarded a $150,000 grant from the National Science Foundation (NSF), with Dr. Mary Regina Boland, C’10, assistant professor of data science, serving as the principal investigator. This funding is part of a larger $1 million collaborative grant involving the University of Pennsylvania, University of Iowa, and University of Virginia.
The project, titled Personal Determinants of Health-Enhanced Machine Learning Models for Early Prediction of Alzheimer's Disease and Related Dementias, aims to advance national health and scientific innovation by developing algorithms, software, and systems that use electronic health records to accurately and early predict Alzheimer’s Disease and Related Dementias (ADRD).
ADRD affects more than 5 million Americans over the age of 65 and is characterized by progressive memory loss, cognitive decline, and personality changes, often leading to dementia and death. With the U.S. population aging—as shown by the “Vintage 2024 Population Estimates from the U.S. Census Bureau, which reported that older adults now outnumber children in 11 states and nearly half of U.S. counties—the prevalence of ADRD is expected to rise.
Unless research identifies methods for early detection and treatment of ADRD, the strain on the nation’s healthcare system will be unprecedented, said Boland.
The project seeks to learn patterns from individuals who have or had ADRD to identify those at risk earlier and detect treatable risk factors. Research shows that personal factors such as education, employment, lifestyle, and family history significantly influence ADRD onset and progression. However, these factors are often buried in unstructured clinical notes and discharge summaries, making them difficult to extract and integrate into predictive models.
To address this challenge, the team will develop a computational platform using machine learning and natural language processing to automatically extract personal risk factors from clinical narratives and incorporate them into predictive models. Boland hopes the project will not only validate known risk factors but also uncover new ones, potentially informing early interventions and therapies.
Understanding patterns of risk factors can help us detect ADRD earlier and explore potential treatments.
Some individuals develop ADRD earlier or progress more rapidly than others, possibly due to varying environmental exposures. Boland noted that differences in toxin exposure, influenced by where people live and work, may contribute to these variations, resulting in diverse ADRD presentations across populations.
Source: https://www.stvincent.edu/news/2025/10/svc-obtains-150,000-national-science-foundation-grant.html
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