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.