2304358
Project Grant
Overview
Grant Description
Sbir Phase I: Developing Artificial Intelligence Models to Predict In-Hospital Clinical Trajectories for Heart Failure Patients -The Broader/Commercial Impact of This Small Business Innovation Research (SBIR) Phase I Project Includes Improving Cardiovascular Management, Personalized Medicine, Inclusivity for Historically Underserved Populations, and Clinical Trial Design.
The Project Could Improve the Health and Wellbeing of Heart Failure (HF) Patients While Saving Billions of Dollars in HF Hospitalization Costs. If the Technology Proves Feasible, It Could Shift the Paradigm of HF Management from Reactive to Proactive.
The Proposed Machine Learning Model Extracts Latent Features and Detects Subtle Patterns from Clinical Data, Which Derives Digital Biomarkers That Can Potentially Enable Novel Phenotype Discovery and Eventually Personalized Medicine.
The Digital Biomarkers Derived from the Proposed Innovation, When Used in Clinical Trials, Could Also Improve Inclusivity and Greater Generalizability of Novel Therapies When Applied to Diverse Populations.
The Proposed Technology Could Enable Clinical Trial Sponsors to Achieve the Desired Statistical Power with Smaller Patient Populations. This, in Turn, Would Enable Faster, Cheaper, and More Effective Clinical Trials.
This Small Business Innovation Research (SBIR) Phase I Project Mitigates the Burden of Heart Failure (HF), Which Afflicts Over 6.5 Million Americans. As the Leading Cause of Hospitalization in the U.S., HF Results in More Than $29 Billion in Hospital Charges and $11 Billion in Hospitalization Costs, Annually.
A Large Portion of Hospitalization Costs Are Driven by Readmissions, with About 20% of Heart Failure Patients Readmitted Within 30 Days of Discharge. The Fundamental Challenge Is the Variability of This Disease.
A Treatment Regimen That Works for One Patient Might Not Work for Another, Even If They Show Similar Symptoms. Anticipating Clinical Trajectories, Treatment Response, and Potential Complications, and Translating Those Insights into Actionable Interventions Is Key to Improving Outcomes for HF Patients.
To Help Clinicians Anticipate a HF Patient's Response to Treatment and Adverse Events During Hospitalization and Enable Personalized Intervention Planning, This Project Will Develop Explainable and Generalizable Multimodal Artificial Intelligence (AI) Models That Predict a HF Patient's Clinical Trajectory Shortly After Admission.
This Technology Is a Methodological Innovation Grounded in Large-Scale, Multi-Center, Clinical Data. The Key Milestone in Phase I Is to Yield a Reasonably Accurate Predictive AI Model, Cross-Validated Between the Data of Two Large Healthcare Systems.
This Award Reflects NSF's Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.- Subawards Are Planned for This Award.
The Project Could Improve the Health and Wellbeing of Heart Failure (HF) Patients While Saving Billions of Dollars in HF Hospitalization Costs. If the Technology Proves Feasible, It Could Shift the Paradigm of HF Management from Reactive to Proactive.
The Proposed Machine Learning Model Extracts Latent Features and Detects Subtle Patterns from Clinical Data, Which Derives Digital Biomarkers That Can Potentially Enable Novel Phenotype Discovery and Eventually Personalized Medicine.
The Digital Biomarkers Derived from the Proposed Innovation, When Used in Clinical Trials, Could Also Improve Inclusivity and Greater Generalizability of Novel Therapies When Applied to Diverse Populations.
The Proposed Technology Could Enable Clinical Trial Sponsors to Achieve the Desired Statistical Power with Smaller Patient Populations. This, in Turn, Would Enable Faster, Cheaper, and More Effective Clinical Trials.
This Small Business Innovation Research (SBIR) Phase I Project Mitigates the Burden of Heart Failure (HF), Which Afflicts Over 6.5 Million Americans. As the Leading Cause of Hospitalization in the U.S., HF Results in More Than $29 Billion in Hospital Charges and $11 Billion in Hospitalization Costs, Annually.
A Large Portion of Hospitalization Costs Are Driven by Readmissions, with About 20% of Heart Failure Patients Readmitted Within 30 Days of Discharge. The Fundamental Challenge Is the Variability of This Disease.
A Treatment Regimen That Works for One Patient Might Not Work for Another, Even If They Show Similar Symptoms. Anticipating Clinical Trajectories, Treatment Response, and Potential Complications, and Translating Those Insights into Actionable Interventions Is Key to Improving Outcomes for HF Patients.
To Help Clinicians Anticipate a HF Patient's Response to Treatment and Adverse Events During Hospitalization and Enable Personalized Intervention Planning, This Project Will Develop Explainable and Generalizable Multimodal Artificial Intelligence (AI) Models That Predict a HF Patient's Clinical Trajectory Shortly After Admission.
This Technology Is a Methodological Innovation Grounded in Large-Scale, Multi-Center, Clinical Data. The Key Milestone in Phase I Is to Yield a Reasonably Accurate Predictive AI Model, Cross-Validated Between the Data of Two Large Healthcare Systems.
This Award Reflects NSF's Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.- Subawards Are Planned for This Award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF22551
Grant Program (CFDA)
Awarding Agency
Place of Performance
Cambridge,
Massachusetts
02139-3359
United States
Geographic Scope
Single Zip Code
Related Opportunity
22-551
Analysis Notes
Amendment Since initial award the End Date has been extended from 02/29/24 to 07/31/25.
Empallo was awarded
Project Grant 2304358
worth $275,000
from in September 2023 with work to be completed primarily in Cambridge Massachusetts United States.
The grant
has a duration of 1 year 10 months and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:Developing Artificial intelligence Models to Predict In-hospital Clinical Trajectories for Heart Failure Patients
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project includes improving cardiovascular management, personalized medicine, inclusivity for historically underserved populations, and clinical trial design. The project could improve the health and wellbeing of heart failure (HF) patients while saving billions of dollars in HF hospitalization costs. If the technology proves feasible, it could shift the paradigm of HF management from reactive to proactive. The proposed machine learning model extracts latent features and detects subtle patterns from clinical data, which derives digital biomarkers that can potentially enable novel phenotype discovery and eventually personalized medicine. The digital biomarkers derived from the proposed innovation, when used in clinical trials, could also improve inclusivity and greater generalizability of novel therapies when applied to diverse populations. The proposed technology could enable clinical trial sponsors to achieve the desired statistical power with smaller patient populations. This, in turn, would enable faster, cheaper, and more effective clinical trials._x000D_ _x000D_ This Small Business Innovation Research (SBIR) Phase I project mitigates the burden of heart failure (HF), which afflicts over 6.5 million Americans. As the leading cause of hospitalization in the U.S., HF results in more than $29 billion in hospital charges and $11 billion in hospitalization costs, annually. A large portion of hospitalization costs are driven by readmissions, with about 20% of heart failure patients readmitted within 30 days of discharge. The fundamental challenge is the variability of this disease. A treatment regimen that works for one patient might not work for another, even if they show similar symptoms. Anticipating clinical trajectories, treatment response, and potential complications, and translating those insights into actionable interventions is key to improving outcomes for HF patients. To help clinicians anticipate a HF patient’s response to treatment and adverse events during hospitalization and enable personalized intervention planning, this project will develop explainable and generalizable multimodal artificial intelligence (AI) models that predict a HF patient’s clinical trajectory shortly after admission. This technology is a methodological innovation grounded in large-scale, multi-center, clinical data. The key milestone in Phase I is to yield a reasonably accurate predictive AI model, cross-validated between the data of two large healthcare systems._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
AI
Solicitation Number
NSF 22-551
Status
(Complete)
Last Modified 7/8/24
Period of Performance
9/1/23
Start Date
7/31/25
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2304358
Additional Detail
Award ID FAIN
2304358
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
CU2YCT1NWFJ5
Awardee CAGE
9CUQ3
Performance District
MA-07
Senators
Edward Markey
Elizabeth Warren
Elizabeth Warren
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $275,000 | 100% |
Modified: 7/8/24