SBIR Phase I: Artificial intelligence platform for secure, collaborative learning across medical institutions

Soar Analytics Llc, Berwyn PA

Investigators

Abstract

The broader impact / commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a collaborative-learning platform that generates accurate population health models from secure access to patient records. The proposed system will overcome privacy concerns to enable AI methods to securely access hundreds of millions of patient records from multiple institutions across the US to learn high-performing predictive models. Models learned from this platform are differentiated due to their training data and help payors, providers, and pharma companies that benefit from early diagnosis and treatment of patients that may have remained undiagnosed. This system will improve patient outcomes and health care system performance. This Small Business Innovation Research (SBIR) Phase I project will address fundamental limitations that can deter medical institutions from sharing patient data to support learning clinical-grade models. Unlike federated learning (FL), neither local data nor local model parameters are shared; rather, local classifiers predict labels for an unlabeled global dataset. Sharing model parameters in FL can violate privacy requirements and expose patient data used for local training. This novel platform is designed to be immune to the “white box” attacks of FL, and its main privacy risk for membership inference is significantly lower. This platform will accurately include information from all subpopulations and will support collaborative learning for multiple ML algorithms, including human-interpretable algorithms that cannot be learned with FL. Accuracy will be evaluated via sensitivity and specificity, privacy via membership vulnerability. Methods compared include federated learning and differential privacy. 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.