Project Grant R35GM160146
- 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 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 federal Project Grant award of $1,479,335.00 from the National Heart Lung and Blood Institute under CFDA 93.837 Cardiovascular Diseases Research aims to leverage observational data and advanced machine learning methods to address gaps in understanding and personalizing treatment for Acute Respiratory Distress Syndrome (ARDS). The key products and services to be delivered include: Developing improved methods for ARDS phenotyping using dynamic clinical data to better understand prognosis...
- This Project Grant award from the National Heart, Lung, and Blood Institute (CFDA 93.837 - Cardiovascular Diseases Research) aims to advance precision medicine for Acute Respiratory Distress Syndrome (ARDS) and sepsis. The $2,295,984 award to the University of California, San Francisco (UCSF) will fund research to address critical unanswered questions about the distinct molecular phenotypes of ARDS and sepsis, and how to optimize real-time phenotyping and integrate analyses of phenotype-specific...
- This Project Grant award of $186,221 from the National Institute of Allergy and Infectious Diseases (NIAID), under the Allergy and Infectious Diseases Research Federal Grant Program (CFDA 93.855), aims to develop personalized therapies for pediatric sepsis. The primary objectives are to augment sepsis phenotypes through the integration of high-frequency physiologic data with electronic health record data, and to retrospectively identify sepsis subtypes that may respond differently to various...
- This Project Grant award of $455,000 from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training Program (CFDA 93.859) aims to decrease sepsis mortality in the pediatric population. The key products and services to be delivered include: Developing an AI-guided early sepsis identification tool to enable timely diagnosis and treatment. Performing sepsis phenotyping and creating digital twin patient models to predict personalized treatment responses....
- This federal Project Grant award of $422,125 from the National Institute of General Medical Sciences (NIGMS), under the Biomedical Research and Research Training program (CFDA 93.859), supports research to assess whether multi-omic data of myeloid-derived suppressor cells (MDSCs) can improve sepsis endotyping and predict clinical trajectories in chronic critical illness (CCI) after sepsis. The overarching goal is to better understand the role of "pathologic myelopoiesis" and MDSC...
- 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 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...
- This Project Grant award of $153,099.00 from the National Heart Lung and Blood Institute under the Cardiovascular Diseases Research program (CFDA 93.837) is funding research to use target trial emulation and g-methods for causal inference from observational data to address gaps in knowledge about the care of patients with acute respiratory distress syndrome (ARDS), with a focus on evaluating dynamic treatment regimes for steroid initiation, duration, and dosing. The research will utilize data...
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 treatment heterogeneity at the individual patient level. This work builds on the grantee's preliminary studies using ITE-based methods to identify heterogeneous patient responses in previously completed clinical trials. The project is being conducted by the University of Pittsburgh, a prominent public research university, from September 1, 2025 through August 31, 2030.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $437.3k | 9/1/25 |