Literature DB >> 18693931

The UMLS Knowledge Source Server: an experience in Web 2.0 technologies.

Karen E Thorn1, Anantha K Bangalore, Allen C Browne.   

Abstract

The UMLS Knowledge Source Server (UMLSKS), developed at the National Library of Medicine (NLM), makes the knowledge sources of the Unified Medical Language System (UMLS) available to the research community over the Internet. In 2003, the UMLSKS was redesigned utilizing state-of-the-art technologies available at that time. That design offered a significant improvement over the prior version but presented a set of technology-dependent issues that limited its functionality and usability. Four areas of desired improvement were identified: software interfaces, web interface content, system maintenance/deployment, and user authentication. By employing next generation web technologies, newer authentication paradigms and further refinements in modular design methods, these areas could be addressed and corrected to meet the ever increasing needs of UMLSKS developers. In this paper we detail the issues present with the existing system and describe the new system's design using new technologies considered entrants in the Web 2.0 development era.

Mesh:

Year:  2007        PMID: 18693931      PMCID: PMC2655838     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  1 in total

1.  The UMLS knowledge source server: an object model for delivering UMLS data.

Authors:  Anantha Bangalore; Karen E Thorn; Carolyn Tilley; Lee Peters
Journal:  AMIA Annu Symp Proc       Date:  2003
  1 in total
  4 in total

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Authors:  Steven D Bedrick; Alejandro Mauro
Journal:  AMIA Annu Symp Proc       Date:  2009-11-14

2.  A Knowledge Intensive Approach to Mapping Clinical Narrative to LOINC.

Authors:  Marcelo Fiszman; Dongwook Shin; Charles A Sneiderman; Honglan Jin; Thomas C Rindflesch
Journal:  AMIA Annu Symp Proc       Date:  2010-11-13

Review 3.  Review of extracting information from the Social Web for health personalization.

Authors:  Luis Fernandez-Luque; Randi Karlsen; Jason Bonander
Journal:  J Med Internet Res       Date:  2011-01-28       Impact factor: 5.428

4.  DIVAS: a centralized genetic variant repository representing 150,000 individuals from multiple disease cohorts.

Authors:  Wei-Yi Cheng; Jörg Hakenberg; Shuyu Dan Li; Rong Chen
Journal:  Bioinformatics       Date:  2015-09-12       Impact factor: 6.937

  4 in total

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