J
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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Proceedings ArticleDOI
Analyzing the Semantics of patient data to rank records of literature retrieval
TL;DR: A significant positive relation is found between physicians' selection of abstracts and two of the methods, and the results encourage the use of clinical data to determine the relevance of medical literature to the care of individual patients.
Journal ArticleDOI
Computer-aided assessment of the generalizability of clinical trial results.
TL;DR: A framework for a standardized evaluation of parameters relevant to determining the external validity of clinical trials to produce a "generalizability score" is proposed and applied to populations of patients with heart failure included in trials, cohorts and registries.
Proceedings Article
Mining cross-terminology links in the UMLS.
Chintan Patel,James J. Cimino +1 more
TL;DR: Using link mining with the UMLS is a promising approach for inter-terminology translation; further research is needed to handle the exponential link growth.
Journal ArticleDOI
Using the UMLS to bring the library to the bedside.
James J. Cimino,Robert V. Sideli +1 more
TL;DR: An algorithm that can be used to convert ICD9 terms to related MeSH terms and the UMLS provide a reasonable resource for facilitating such conversions is presented.
Journal ArticleDOI
Formal representation of patients' care context data: the path to improving the electronic health record
TL;DR: A collection of concept-relationship-concept tuples to formally represent patients’ care context data to inform electronic health record (EHR) development is developed and proposed to improve EHR navigation, data entry, learning health systems, and decision support.