NHLBI ENTERPRISE ARCHITECTURE AND CYBER SECURITY SUPPORT FOR DATA SCIENCE PROGRAMS

Investigators

Abstract

Over the last several decades, NHLBI has invested in creating a significant resource for research and development by supporting the creation of many observational, epidemiological, and longitudinal datasets related to heart, lung, blood and sleep phenotypes, with the aim of uncovering insights that may be leveraged toward novel therapeutic, interventional, or preventive strategies resulting in improved patient outcomes. New technologies and favorable cost trajectories have enabled detailed characterization of these study participants including whole genome sequencing (and other omics) and imaging on hundreds of thousands of participants. Together this data coupled with animal and cellular models increase opportunities for data-driven translational science. We have fully entered the “Big Data” arena, in which we encounter both unprecedented opportunities as well as challenges. Current paradigms for analyzing and combining these datasets are limited by both practical and conceptual constraints. Here we introduce the NHLBI BioData Catalyst, a novel ecosystem of platforms, tools, and data to enable and accelerate scientific discovery. A centralized architecture has not been a core focus on the BDC teams and there are challenges on having non-standard and disparate architectures work within the ecosystem. There is an opportunity to define and implement standards and perform architecture services within the program to strengthen the back-end while improving the user experience on BDC. Additionally there is a need for cybersecurity support as the program aligns systems to the tailored risk management framework (RMF). The NIH’s Artificial Intelligence/Machine Learning (AI/ML) Consortium to Advance Health Equity and Research Diversity (AIM-AHEAD) Program establishes partnerships and funds projects to build AI research capacity. Specifically, AIM-AHEAD seeks to improve capacity within underrepresented communities and contributes to advancing equity-driven AI/ML approaches that minimize bias in the healthcare space while addressing national health disparities. The AIM-AHEAD data infrastructure approach utilizes a wider variety of solutions to meet the needs of the communities participating in the program. AIM-AHEAD utilizes Cloud platforms to integrate data storage, computing cycles, security, and, often, analysis tools for geographically distributed users and groups. Distributed or federated learning approaches are more appropriate when data cannot be pooled and are also being explored in the program.