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James J. Cimino

Researcher at University of Alabama at Birmingham

Publications -  390
Citations -  14092

James J. Cimino is an academic researcher from University of Alabama at Birmingham. The author has contributed to research in topics: Unified Medical Language System & Information needs. The author has an hindex of 58, co-authored 367 publications receiving 12899 citations. Previous affiliations of James J. Cimino include Duke University & Rutgers University.

Papers
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Journal ArticleDOI

Representing the UMLS as an object-oriented database: modeling issues and advantages.

TL;DR: The authors present a representation to support the user's comprehension and navigation of the UMLS, and introduces intersection classes to help expose and resolve modeling problems in the U MLS.
Journal ArticleDOI

Promoting patient safety and enabling evidence-based practice through informatics.

TL;DR: The role of informatics in promoting patient safety and enabling evidence-based practice (EBP), 2 significant aspects for assuring healthcare quality, are highlighted and key recommendations for education, practice, policy, and research are provided.
Proceedings Article

Theoretical, empirical and practical approaches to resolving the unmet information needs of clinical information system users.

TL;DR: An Infobutton Manager is constructed to match the data being reviewed by clinicians with context-appropriate infobuttons and can construct an "infobutton" that links the clinical data to an on-line information resource.
Proceedings ArticleDOI

PERSIVAL, a system for personalized search and summarization over multimedia healthcare information

TL;DR: Progress is presented on developing a system, PERSIVAL, that is designed to provide personalized access to a distributed patient care digital library, using the secure, online patient records at New York Presbyterian Hospital as a user model.
Proceedings Article

The MEDLINE Button.

TL;DR: A computerized method for performing bibliographic searches directly from patient data involving five steps: identifying specific patient data which raises a question in the mind of the user, selection (from a list of generic questions) of a small number of questions which fit the selected patient data, automated translation of the patient data into appropriate terms used for bibliographical indexing, and conversion of the question selected by the user into a search strategy.