John H. Holmes, PhD, FACE, FACMI, FIAHSI

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John H. Holmes, PhD, FACE, FACMI, FIAHSI

Professor of Medical Informatics in Epidemiology

John H. Holmes, PhD, is Professor of Medical Informatics in Epidemiology at the University of Pennsylvania Perelman School of Medicine. He is the Associate Director for Medical Informatics of the Penn Institute for Biomedical Informatics and is Past-Chair of the Doctoral Program in Epidemiology. He has mentored or co-mentored over 50 pre- and post-doctoral students in informatics or epidemiology, and has developed curricula for graduate training in epidemiology and biomedical informatics as well as short courses in these disciplines. Dr. Holmes has been recognized nationally and internationally for his work on developing and applying new approaches to mining epidemiologic surveillance data, as well as his efforts at furthering educational initiatives in clinical research. Dr. Holmes’ research interests are focused on the intersection of medical informatics and epidemiologic research, specifically evolutionary computation and machine learning approaches to knowledge discovery in clinical databases, deep electronic phenotyping, interoperable information systems infrastructures for epidemiologic surveillance, and their application to a broad array of clinical domains. He has been deeply engaged in simulation through agent-based and network models of social, behavioral, and policy issues that affect health in the context of ever-changing environments. Dr. Holmes is an elected Fellow of the American College of Medical Informatics (ACMI) and the American College of Epidemiology (ACE), and an elected Fellow of the International Academy of Health Sciences Informatics (IAHSI). He is the Vice Chair of the ACE Ethics Committee.

Content Area Specialties

Treatment regimen adherence, cancer epidemiology, cardiovascular epidemiology, infectious diseases, injury, medical informatics, patient-oriented research, pharmacoepidemiology, prevention, public health, pulmonary epidemiology
 

Methodology Specialties

Agent-based modeling, machine learning approaches to knowledge discovery in databases, deep electronic phenotyping, interoperable information systems infrastructures for epidemiologic surveillance

About Us

To understand health and disease today, we need new thinking and novel science —the kind  we create when multiple disciplines work together from the ground up. That is why this department has put forward a bold vision in population-health science: a single academic home for biostatistics, epidemiology and informatics. 

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