Project Grant R35GM159939
- This $437,250 project grant, awarded by the National Institute of General Medical Sciences (NIGMS) under the Biomedical Research and Research Training program (CFDA 93.859), aims to improve the characterization of individualized treatment effects in critical illness, particularly for sepsis and acute respiratory distress syndrome (ARDS). The research will focus on developing computational approaches to estimate individualized treatment effects (ITEs) using machine learning, which can uncover...
- This Project Grant award of $750,561 from the National Heart Lung and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837) will fund the SMART-SEPSIS study. The study aims to use electronic health record data and artificial intelligence/machine learning to model individual patient response to sepsis treatment and promote early but reasoned application of the hour-1 sepsis treatment bundle for patients with suspected community-onset lung sepsis. The goal is...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program provides $612,137 to the University of Maryland, College Park to develop a new machine learning framework for anesthesia risk stratification and decision support. The primary objectives are to: 1) automate the processing of electronic anesthesia data, 2) create a semi-supervised generative adversarial network for risk stratification, 3) build an interpretable deep...
- This federal Project Grant award of $426,479 from the National Institute of General Medical Sciences (NIGMS), under the Biomedical Research and Research Training program (CFDA 93.859), will support research toward developing statistical and computational tools to enable precision risk stratification, diagnosis, and treatment of medical conditions. The research will focus on integrating and synthesizing data from diverse sources such as biobanks, electronic health records, and clinical registries...
- 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 $696,824 federal Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), supports research at the University of North Carolina at Chapel Hill (UNC-CH) to develop robust and interpretable multi-modal AI/ML models for precision medicine. The project aims to address key challenges in integrating complex, heterogeneous biomedical data...
- This Project Grant award from the National Institute of Neurological Disorders and Stroke (NINDS) under the Extramural Research Programs in the Neurosciences and Neurological Disorders (CFDA 93.853) provides funding of $224,265 to the University of Pittsburgh to conduct research aimed at improving prognostication for patients with hypoxic-ischemic brain injury following sudden cardiac arrest. The project seeks to use principles of decision science and organizational behavior to develop and...
- The University of Pittsburgh received a Project Grant award of $365,103 from the National Institute of Environmental Health Sciences (CFDA 93.879 - Medical Library Assistance program) on September 5, 2025. The purpose of this award, titled "ARISE-CARE: ADVANCING REHABILITATION FOR STROKE PATIENTS WITH AI TO ELEVATE THERAPY CARE", is to develop and evaluate the use of generative artificial intelligence (AI) techniques to automate and streamline clinical documentation for stroke...
- This Project Grant award from the National Institute of General Medical Sciences (NIGMS) under the Biomedical Research and Research Training program (CFDA 93.859) is funding the development of next-generation machine learning-based predictive models for early detection of cured and uncured disease states. The $278,104 grant awarded on May 1, 2025 will support research conducted by the University of Texas at Arlington (UTA) to create computationally efficient models that can leverage patient...
- This $194,022 Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) supports research led by the Massachusetts General Hospital (MGH) to develop advanced deep learning models for predicting therapeutic response in cancer patients with brain metastases. The grant will fund the candidate, an oncologist at MGH, to build upon his expertise in...
This Project Grant award of $433,710 from the National Institute of General Medical Sciences (NIGMS) under the Biomedical Research and Research Training program (CFDA 93.859) aims to develop a comprehensive machine learning-driven prescriptive clinical decision support system for shock and associated critical care conditions. The principal investigator is Dr. Joo Heung Yoon from the University of Pittsburgh. The key objectives are to: 1) Create a multimodal foundation model for ICU patients using self-supervised pre-trained models, 2) Develop an overarching decision support engine that handles competing clinical interests, and 3) Implement post-deployment analysis to ensure safety of using machine learning models at the bedside. This research project will run from August 15, 2025 to July 31, 2030 and focus on addressing critical bottlenecks in deploying effective ML-driven clinical decision support systems, using shock and critical care conditions as the example use case.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $433.7k | 8/14/25 |