Mary Regina Boland, MA, MPhil, PhD
Assistant Professor, Department of Biostatistics, Epidemiology & Informatics
University of Pennsylvania
Dr. Mary Regina Boland, is an Assistant Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics in the Perelman School of Medicine at the University of Pennsylvania. She is also a Senior Fellow in the Institute for Biomedical Informatics and an Affiliate Member of the Center for Excellence in Environmental Toxicology at the University of Pennsylvania. Mary Regina received her PhD in Biomedical Informatics from Columbia University along with two Masters degrees. Her work focuses on developing novel data mining and machine learning algorithms that integrate data from Electronic Health Records, observational health data and genetics. Specifically, her work centers on the relationship between environmental exposures during the prenatal/perinatal period the subsequent modulations in disease risk.
Dr. Boland lead an expansion of her work on birth month and lifetime disease risk that has been accepted at JAMIA (currently in press, 2017). She is also working on ongoing collaborations in this same area with members of the OHDSI community and beyond.
MR Boland, Z Shahn, D Madigan, G Hripcsak, NP Tatonetti. Birth Month Affects Lifetime Disease Risk: A Phenome-Wide Method. J Am Med Inform Assoc. 2015. (In Press). Covered By Over 450 Different News Outlets from N America, Europe, Asia, Middle East, Australia, Africa. Rated No. 1 By Altmetric for articles published in J Am Med Inform Assoc.
MR Boland, NP Tatonetti, G Hripcsak. Development and Validation of a Classification Approach for Extracting Severity Automatically from Electronic Health Records. Journal of Biomedical Semantics (JBMS). 2015; 6:14. Highly Accessed.
MR Boland, NP Tatonetti. Are All Vaccines Created Equal? Using Electronic Health Records to Discover Vaccines Associated With Clinician-Coded Adverse Events. AMIA Summits on Translational Science Proceedings; 2015; 196-200.
1st Place Winner of Distinguished Student Paper Award 2015 AMIA Translational Summit.
MR Boland, NP Tatonetti, G Hripcsak. CAESAR: a Classification Approach for Extracting Severity Automatically from Electronic Health Records. Intelligent Systems for Molecular Biology Phenotype Day. 2014; Boston, MA.