# NIH grant: SCH: Integrated AI and Sensing Systems for Mitigating Heart Failure Readmissions in High-Risk  Groups with Obesity (1R01NR022545-01) — retained record

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[original source ↗](https://reporter.nih.gov/project-details/1R01NR022545-01)

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NIH grant 1R01NR022545-01: SCH: Integrated AI and Sensing Systems for Mitigating Heart Failure Readmissions in High-Risk  Groups with Obesity
Institute: {'code': 'NR', 'abbreviation': 'NINR', 'name': 'National Institute of Nursing Research', 'admin_org_id': '12500052', 'admin_funding_url': ''}. PI: Wei  Gao. Award: $1200000.
Start: 2026-09-25T00:00:00.
Heart failure (HF) is a common, costly and potentially fatal cardiac condition, and the leading cause of 
hospitalization in senior adults. HF patients’ readmissions after hospital discharge are frequent and nearly 
always associated with worse clinical outcomes, and reducing such readmissions is the key to improve 
the HF survival rate. Especially in the group of obese patients, this readmission rate could be higher by 
another 30%, due to their higher chances of residual congestion after hospital discharge. A possible 
solution is to continuously monitor the patients’ cardiac vital signs and predict any HF readmission risk 
from these vital signs, but current wearables are highly inaccurate when measuring cardiac vital signs in 
the high-risk group with obesity. The goal of this research is to fundamentally address the above 
incapability and reduce HF readmissions in high-risk group with obesity, by developing integrated AI and 
sensing systems. Custom wearable sensors will monitor HF patients’ cardiac vital signs, and new 
machine learning (ML) models will predict the HF readmission risk based on cross-modal correlations 
among multimodal sensory data. The proposed research aims to ensure accuracy, adaptability and 
reliability of such prediction. First, we will design biomagnetism-based sensing systems for precise 
monitoring of cardiac vital signs with the presence of extra subcutaneous fat. Second, we will extract 
cross-modal features from multimodal sensory data, and apply these features to Large Language Models 
(LLMs) for predicting HF readmission risk in the natural language domain. Third, to ensure the prediction 
model’s generality, we will continuously fine-tune our cardiac LLMs in a distributed manner out of clinic, by 
developing new federated learning (FL) techniques. Fourth, when some input data modalities are 
unavailable, we will develop incomplete multimodal learning techniques based on knowledge distillation, 
to distill useful information related to the missing modalities. The proposed technologies will be evaluated 
via lab-controlled testing, experimentation over healthy volunteers and efficacy verification over subjects 
with HF conditions. The successful completion of this project will develop cutting-edge embedded sensing 
and AI techniques to ensure accurate monitoring of cardiac vital signs on HF patients with obesity, hence 
enabling real-time tracking and prediction of HF readmission risk out of clinic and significantly enhancing 
the detection and prevention of this fatal cardiac condition.
```



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