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Public Biography
Dina Demner-Fushman, MD, PhD leads research in information retrieval and natural language processing; providing clinical decision support through linking evidence (text and images) to patientsĂ­ data; answering clinical and consumer health questions; and extracting information from clinical text. Dr. Demner-Fushman earned her doctor of medicine degree from Kazan State Medical Institute in 1980, and clinical research Doctorate (PhD) in Medical Science degree from Moscow Medical and Stomatological Institute in 1989. She earned her MS and PhD in Computer Science from the University of Maryland, College Park in 2003 and 2006, respectively. She earned her BA in Computer Science from Hunter College, CUNY in 2000. Dr. Demner-Fushman is a lead investigator in several NLM projects in the areas of Information Extraction for Clinical Decision Support, EMR Database Research and Development, and Image and Text Indexing for Clinical Decision Support and Education. The outgrowths of these projects are the evidence-based decision support system in use at the NIH Clinical Center since 2009, an image retrieval engine, Open-i, launched in 2012, and an automatic customers' requests answering service that supports NLM customer services since May 2014. She is the author of more than 160 articles and book chapters in the fields of information retrieval, natural language processing, and biomedical and clinical informatics. She has co-authored a textbook in Biomedical Natural Language Processing published in 2014.

Historic ACMI Biography

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Dr. Demner-Fushman received a doctoral degree in Medicine and Dentistry and PhD degree in Immunology from universities in the Soviet Union, and practiced as an orthodontist in Kazan in the USSR and in Frankfurt, Germany. She emigrated to the US and continued her education, receiving a bachelors degree in computer science from Hunter College in New York, and Masters and PhD degrees in computer science from the University of Maryland. She undertook a postdoctoral fellowship in medical informatics at the Lister Hill Center, and in 2007 became a staff scientist at the National Library of Medicine. At NLM Dr. Demner-Fushman has been a major contributor in the application of natural language processing and information management for enhancing clinical infrastructure and health care delivery. She developed an innovative method combining UMLS ontological knowledge with clinical knowledge from the literature. This approach, which was originally devised for clinical question answering, is being applied to automatic extraction of information needs from NIH Clinical Center records. She has been recognized as a leading biomedical NLP researcher as evidenced by her role since 2007 in organizing the BioNLP workshops of the Association for Computational Linguistics, which have attracted a growing number of mainstream computational linguists and computer scientists. At the time of her election, Dr. Demner-Fushman had contributed as an author to 87 peer reviewed publications, and creation of a number of novel applications, including InfoBot, a Repository for Informed Decision Making (or RIDeM), methods for automatic annotational and retrieval of images extracted from publications known as iMEDLINE, and HLDISCOVERY, which is a de-identified database system for clinically derived data. She has also been instrumental in adapting related information extraction techniques for NLMĂ­s successful participation in several biomedical natural language processing competitions. Her election to fellowship recognizes these technical and organizational contributions.

Affiliations

The American College of Medical Informatics

ACMI is a college of elected Fellows from the U.S. and abroad who have made significant and sustained contributions to the field of medical informatics. It is the central body for a community of scholars and practitioners who are committed to advancing the informatics field.

Year Elected:
2011
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Natural Language Processing
Working Group

Past Chair 2023

The mission of the Natural Language Processing is to develop, apply, and promote natural language processing in biomedical science, patient care, public health and biomedical education.

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