I-Corps: Artificial Intelligence Models to Improve Heart Failure Management

Award ID: 2234141

The broader impact/commercial potential of this I-Corps project is the development of technology that could improve heart failure management for many stakeholders, including hospitals, integrated health organizations (IHOs), medical insurers, electronic health records (EHRs) vendors, medical device manufacturers, pharmaceutical companies, and contract research organizations (CROs). Hospitals, IHOs and medical insurers may see value in the technologys potential to improve patient outcomes, increase productive lifespans, and reduce healthcare costs. Pharmaceutical companies, medical device manufacturers, and CROs may also see value in the technologys potential to expedite and improve patient recruitment for clinical trials. Furthermore, the technology could ultimately be applied to adjacent chronic conditions, such as chronic obstructive pulmonary disease (COPD) and kidney failure. The vision is to develop an artificial intelligence (AI) platform that integrates siloed patient data to improve management of chronic conditions. This I-Corps project is based on the development of machine learning (ML) algorithms that stratify and phenotype heart failure patients to identify those at risk of adverse events and recommend appropriate interventions. The ML models are trained on millions of de-identified patient records including chest radiographs and numerical data (e.g., vital signs and lab tests). The trained artificial intelligence (AI) models identify subtle indicators and patterns from the clinical data and stratify patients on a granular level. The deep phenotyping has the potential to personalize intervention plans for heart failure management. The team developed machine learning (ML) algorithms to quantitatively assess patient fluid overload from chest radiographs, finding chest radiographs contain information that can be used to predict readmissions of heart failure patients. The 30-day readmission rate correlates well with the pulmonary edema severity that the AI model estimates based on the pre-discharge chest x-ray images. 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.

NSF Program Director: Molly Wasko