D
Dina Demner-Fushman
Researcher at National Institutes of Health
Publications - 267
Citations - 9172
Dina Demner-Fushman is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Question answering & Image retrieval. The author has an hindex of 45, co-authored 267 publications receiving 7707 citations. Previous affiliations of Dina Demner-Fushman include University of Maryland College of Information Studies & University of Maryland, College Park.
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Proceedings Article
Evaluation of PICO as a Knowledge Representation for Clinical Questions
TL;DR: The PICO framework is primarily centered on therapy questions, and is less suitable for representing other types of clinical information needs, and its value as a tool to assist physicians practicing EBM is reaffirmed.
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Methodological Review: What can natural language processing do for clinical decision support?
TL;DR: This review focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS, and the current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals.
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Preparing a collection of radiology examinations for distribution and retrieval
Dina Demner-Fushman,Marc D. Kohli,Marc B. Rosenman,Sonya E. Shooshan,Laritza Rodriguez,Sameer Antani,George R. Thoma,Clement J. McDonald +7 more
TL;DR: In this article, a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database was presented, where the authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval.
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Frontiers of biomedical text mining: current progress
TL;DR: The current state of the art in biomedical text mining or 'BioNLP' in general is reviewed, focusing primarily on papers published within the past year.
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Answering Clinical Questions with Knowledge-Based and Statistical Techniques
Dina Demner-Fushman,Jimmy Lin +1 more
TL;DR: A series of knowledge extractors are developed, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts, and which significantly outperforms the already competitive PubMed baseline.