Project Grant 2500342
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program provides $375,840 to develop advanced machine learning models and algorithms that can accurately and early predict the onset of Alzheimer's disease and related dementias (ADRD) using electronic health record data. The project aims to extract personal risk factors for ADRD, such as education, employment, and lifestyle, from unstructured clinical notes and leverage...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program Project Grant award of $324,160 supports the development of novel machine learning and natural language processing algorithms to automatically extract personal risk factors from electronic health records for early prediction of Alzheimer's disease and related dementias (ADRD). The project aims to integrate these personal risk factors, such as education, employment, and lifestyle, with...
- This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) provides $150,000.00 to The University of Iowa to advance national health and promote science and technology development. The project aims to develop a computational platform using novel machine learning and natural language processing techniques to automatically extract personal risk factors from electronic health records and leverage them for accurate and early...
- This four-year $1.15 million Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) supports research at the University of Michigan to develop techniques for learning from longitudinal observational clinical data in the presence of noise and confounding to tackle progressive diseases. The University will create shareable electronic health record-based definitions of mild cognitive impairment and Alzheimer's disease, and develop...
- This $170,000 federal Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop robust and human-aligned deep learning techniques for analyzing medical sensor time-series data. The primary goals are to: 1) identify input confounders that lead to spurious correlations in time-series data, 2) design knowledge-editing strategies to correct these spurious correlations, and 3) investigate the techniques...
- This National Science Foundation (NSF) Project Grant award to Wake Forest University, under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to develop a "Neuron Twin" computational system that simulates the human brain to improve understanding and predictions related to Alzheimer's disease. The $501,329 five-year project will leverage deep learning and multiscale modeling to jointly analyze multimodal data, including genetic, neuroimaging, and...
- This $317,591 federal Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to enhance personalized healthcare through the use of large language models (LLMs) and novel memory semiconductor devices. The project aims to develop efficient retrieval-augmented generation (RAG) techniques for LLM personalization, focusing on reducing latency and hardware overhead through algorithm-hardware...
- This $224,994 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program supports the development of a new framework for digital twin modeling of Alzheimer's disease (AD). The University of Illinois project aims to combine clinical data, biomedical research, and advanced computational methods to create personalized digital twins that can predict disease progression and evaluate treatment options. The digital twin models...
- This $348,227 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to improve the scalability and effectiveness of large language models (LLMs) used in healthcare applications. The key objectives are to develop an evaluation framework to address issues like factual and faithfulness hallucinations in LLM outputs, and to introduce innovative reinforcement learning methods to better align LLM...
- The National Science Foundation (NSF) awarded a $175,007 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to The Pennsylvania State University (Penn State) for a collaborative research project titled "Advancing Alzheimer's Disease Understanding and Treatments Through Digital Twin Modeling." The project develops a new framework for digital twin modeling of Alzheimer's disease, combining clinical data, biomedical research, and advanced...
This federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) program provides $150,000.00 to Saint Vincent College to develop algorithms, software, and systems that can train machine learning models on electronic health records (EHRs) for accurate and early prediction of Alzheimer's disease and related dementias (ADRD). The project aims to create computational tools that can automatically extract personal risk factors, such as education, employment, and lifestyle history, from EHR clinical narratives and leverage them to improve the timeliness and accuracy of ADRD prediction. By exploring the interaction between personal and clinical factors in disease development, this project has the potential to transform approaches to early detection and management of complex neurological disorders. The award period runs from October 1, 2025, to September 30, 2029.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 8/4/25 |