Princeton Precision Health develops novel computational, data-driven approaches and theoretical frameworks to build predictive models of individual health risks and disease outcomes. We aim to leverage the vast amount of diverse data becoming available along with cutting edge computational technologies to uncover how environmental and social contexts interact with genetic and molecular processes, enabling more accurate predictions of health outcomes and more personalized, effective interventions for all.
Our Approach
Harnessing Interdisciplinary Expertise
We unite experts across fields–including sociology, psychology, computer science, engineering, genomics, environmental science, epidemiology, medicine, and more–to address the complexity of human health. By integrating expertise, methods, and perspectives, we unlock deeper and otherwise inaccessible insights.
Leveraging AI to Integrate Diverse Data
We bring together different large-scale datasets, including genomic, clinical, environmental, behavioral and social. Using AI and other advanced computational methods, we identify systematic patterns, associations, and predictions on an unprecedented depth and scale.
Our Definition of “Precision”
By "precision" we mean recognizing, modeling, and addressing the inherent differences among individuals and environments to improve accuracy and recognize and reduce bias. This leads to models and recommendations that are more accurate and precise for all.
Uncovering the “Why” Behind Interactions
Our approach focuses on understanding the mechanisms behind how different risks and variables interact to shape health outcomes. We integrate rigorous models from various disciplines to uncover the “why” behind these interactions, resulting in accurate and testable predictions.
