This Project Grant award, valued at $250,000.00, was granted by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program. The award will support a research project titled "Deep Learning for Survival Analysis, Causal Inference, and Conformal Inference" at the University of California, Davis. The key objectives of this 3-year project are to: 1) develop new hypothesis testing procedures for deploying deep learning methods in survival...
This Project Grant from the National Science Foundation Division of Information and Intelligent Systems provides $625,000 to Duke University to develop an interpretable artificial intelligence framework for improving care of critically ill patients. The framework incorporates novel matching techniques known as Almost-Matching-Exactly to analyze observational data from patient treatment and emulate a randomized controlled trial. By matching each treated patient to similar untreated patients,...
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 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will advance the state-of-the-art in nonparametric learning for high-dimensional survival analysis. The $100,000 award, effective July 1, 2024 through June 30, 2027, will support the development of novel supervised embedding and robust nonparametric methods for causal inference and sequential decision-making on high-dimensional survival data. The research aims to provide...
This federal Project Grant award, provided by the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences Program (CFDA 47.049), aims to develop novel Bayesian joint models for analyzing complex high-dimensional health data. The award of $131,615 will support research to associate longitudinal health measurements, such as biomarkers and clinical information, with time-to-event outcomes like disease progression or survival. The project will...
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 $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...
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 $546,385 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering program (CFDA 47.070) will support the development of a machine learning framework for training models across hospitals to address screening and treatment disparities in breast cancer. Specifically, the grant will fund research at Stanford University from September 2022 to August 2026 to create a fair federated representation learning algorithm and framework that can train...
This $170,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is supporting research by North Carolina State University (NC State) to develop robust and human-aligned deep learning techniques for analyzing medical sensor time-series data. The key objectives are to: 1) identify input confounders that can lead to spurious correlations in time-series deep learning models, 2) design strategies to...
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 representation for this complex data, handling long-term irregularly spaced inputs, and integrating customized model interpretability through collaboration with domain experts. The evaluation of these methods will primarily use the publicly available MIMIC critical care dataset, as well as an additional dataset provided by clinicians working on heart failure. This award reflects the NSF's mission to strengthen the nation's scientific enterprise and enhance understanding of major problems, in this case by advancing survival analysis techniques to address crucial healthcare challenges.