Precision heaLth ANd Exposome (PLANE) Lab
We strive to develop exposome data integration and machine learning algorithms to better discover actionable patterns from big health data. Our research considers the totality of the biological, lifestyle, and environmental factors that impacts population health and provides personalized solutions that allow individuals and neighborhoods to improve their health and wellness. Specifically, we are interested in the following topics:
- Precision Health
- Machine Learning
- Integration of Multi-Type, Multi-Scale Health Data
- Environmental, Cardiovascular, and Perinatal Epidemiology
- Health Disparities
Ongoing Research Projects
- The External Exposome and COVID-19 Severity. NIH/NIEHS. Contact PI.
- Assessing the Feasibility of a Universal Prenatal Screening Tool to Identify Women at Risk of Pregnancy-Associated Death in Florida. NIH/NCATS. MPI.
- The Total Environment and Hypertensive Disorders of Pregnancy: A Precision Public Health Approach. American Heart Association. PI.
- Spatiotemporal Analysis of HIV Drug Resistance. Florida Department of Health. Co-I.
- Florida Development in Early Childhood: Adversity and Drug Exposure (FL-DECADE) Study. NIH/NIDA. Co-I.
- Artificial Intelligence-aided Personalization on Dual Antiplatelet Therapy for Patients Underwent Coronary Stent Implantation Using Large-scale Electronic Health Records. American Heart Association. Co-I.
: Our lab would like to thank generous support from NIH, American Heart Association, UF Health/Shands, HealthStreet, Florida Department of Health, the UF College of Public Health and Health Professions, the UF College of Medicine, the UF Clinical and Translational Science Institute, and UF.