Metabolomics:Open Access

Peter L Elkin

Peter L Elkin
Editor-in-Chief
Professor
Mount Sinai School of Medicine, Center for Biomedical Informatics, USA

Biography

Dr. Peter L Elkin has served as a tenured Professor of Medicine at the Mount Sinai School of Medicine. In this capacity, he was the Center Director of Biomedical Informatics, Vice-Chairman of the Department of Internal Medicine and the Vice-President of Mount Sinai hospital for Biomedical and Translational Informatics. Dr. Elkin has published over 120 peer reviewed publications. He received his Bachelors of Science from Union College and his MD from New York Medical College. He did his Internal Medicine residency at the Lahey Clinic and his NIH/NLM sponsored fellowship in Medical Informatics at Harvard Medical School and the Massachusetts General Hospital. Dr. Elkin has been working in Biomedical Informatics since 1981 and has been actively researching health data representation since 1987. He is the primary author of the American National Standards Institute’s (ANSI) national standard on Quality Indicators for Controlled Health Vocabularies ASTM E2087, which has also been approved by ISO TC 215 as a Technical Specification (TS17117). He has chaired Health and Human Service’s HITSP Technical Committee on Population Health. Dr. Elkin served as the co-chair of the AHIC Transition Planning Group. Dr. Elkin is a Master of the American College of Physicians and a Fellow of the American College of Medical Informatics. Dr. Elkin chairs the International Medical Informatics Associations Working Group on Human Factors Engineering for Health Informatics. He was awarded the Mayo Department of Medicine’s Laureate Award for 2005. Dr. Elkin is the index recipient of the Homer R Warner award for outstanding contribution to the field of Medical Informatics.

Research Interest

His research interests are the application of computer science and mathematics to biology and health. He design, implement and apply modern Ontologies in support of the biological sciences. His laboratory has compiled a genomic data analysis pipeline and they have built accurate natural language processing services. These services have abstracted phenotypes from clinical records and reports, facts from the literature in support of marker discovery and assertional knowledge from text based databases.