The National Science Foundation (NSF) awarded a $350,000 Project Grant under the Biological Sciences program (CFDA 47.074) to the University of Illinois in Chicago. The grant funds collaborative research to develop novel machine learning and natural language processing methods for early identification of patients at risk of chronic conditions like type 2 diabetes and congestive heart failure. The project aims to mine heterogeneous healthcare data, protect patient privacy, and create fair,...
This $250,000 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) supports collaborative research to develop advanced statistical tools for efficient integrative analysis of electronic health records and genomics data. The key goals are to: 1) devise data-driven algorithms with theoretical optimality guarantees for transfer learning in areas like high-dimensional...
This $250,000 Project Grant award from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports the development of algorithms for real-time dynamic risk identification and monitoring of streaming data, particularly in the domains of electronic medical records, mobile health, and supply chain. The key objectives are to create a unified framework for dynamic risk detection that can be incorporated into...
This Project Grant award of $350,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of intelligent anonymization methods for preserving the privacy of clients' bio-signals while retaining data utility for clinical purposes. The key products and services to be delivered under this 3-year award (10/1/2024 - 9/30/2027) include: Designing reinforcement learning-guided generative deep learning...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will develop methods to safely and securely make biomedical "dark data" accessible for research without compromising privacy. The $237,951 award to the University of Washington will create open but privacy-preserving replicas of sensitive health datasets, enabling researchers to find, access, and reuse this data to support beneficial AI...
The National Science Foundation (NSF) awarded a $1,183,690 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to The Regents of the University of California, San Francisco (UCSF). The grant, titled "Personalized Machine Learning for Repeat Adverse Health Events Using Novel Multimodal Self-Supervised Pretraining Methods," supports the development of artificial intelligence (AI) models that can predict complex health outcomes like substance use...
This $250,000 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports research at Arizona State University to develop intelligent anonymization methods that preserve the privacy of clients' bio-signals while retaining data utility for clinical purposes. The project aims to create modular and scalable anonymization models suitable for bio-signals from both clinical and everyday wearable...
This $500,000 Project Grant award from the National Science Foundation's Division of Mathematical Sciences supports the development of novel deep learning techniques for interpretable survival analysis of complex longitudinal healthcare data. The project aims to create a unified deep learning model that can effectively analyze multi-modal data, such as text, images, and lab values, collected at irregular intervals to predict patient outcomes. Key objectives include providing a unified feature...
This $1.2 million project grant from the National Science Foundation's Engineering program (CFDA 47.041) will fund research at Stanford University from September 2022 to August 2026 to develop digital tools and algorithms to improve diabetes care delivery and health equity. Specifically, the university will leverage sensor data and machine learning techniques to better understand patient types and needs, allocate limited provider resources efficiently, and design interpretable treatment...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) federal Project Grant award, with CFDA number 47.070, provides $182,477 in funding to Tufts University to develop "decision-aware" machine learning methods that can directly satisfy stakeholder goals in applications such as detecting heart disease and predicting opioid overdoses. The 5-year project, starting on July 1, 2024, will focus on three key technical innovations: 1) decision-aware...
This $320,502 Project Grant award from the National Science Foundation (NSF) Division of Information and Intelligent Systems is for a collaborative research project titled "Knowledge Discovery from Highly Heterogeneous, Sparse and Private Data in Biomedical Informatics." The research aims to mine healthcare data to identify patients likely to develop chronic conditions like type 2 diabetes and heart failure, and to develop models for opportunistic screening, particularly for underserved populations.
The project will address challenges related to integrating diverse data types, handling missing data and privacy protections, and developing fair and explainable machine learning models. The research will explore new approaches to temporal data analysis, data integration, de-identification, and concept/relation extraction. The work aligns with the NSF's Computer and Information Science and Engineering (CISE) program, which supports computing and information science research to enable discovery and innovation. This award does not include any sub-awards.