Literature DB >> 15360791

Vaidurya--a concept-based, context-sensitive search engine for clinical guidelines.

Robert Moskovitch1, Alon Hessing, Yval Shahar.   

Abstract

A major problem in the effective use of clinical guidelines is fast and accurate access at the point of care. Thus, we are developing a digital electronic guideline library (DeGeL) and a set of tools for incremental conversion of free-text guide-lines into increasingly machine-comprehensible representations, which support automated application. Even if guidelines are represented in electronic fashion, care providers need to be able to quickly retrieve the guidelines that best fit the clinical situation at hand. We describe Vaidurya, a search and retrieval engine that exploits the hybrid nature of guideline representation in the DeGeL architecture. Vaidurya can use not only free-text keywords, but also multiple semantic indices along which the guidelines are classified, and the mark up of guidelines in DeGeL, using the semantic roles of one or more guideline-representation languages. Preliminary evaluation of Vaidurya in a standard information task and a large guide-line repository is encouraging; formal evaluation is under way.

Mesh:

Year:  2004        PMID: 15360791

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  4 in total

1.  A comparative evaluation of full-text, concept-based, and context-sensitive search.

Authors:  Robert Moskovitch; Susana B Martins; Eytan Behiri; Aviram Weiss; Yuval Shahar
Journal:  J Am Med Inform Assoc       Date:  2007-01-09       Impact factor: 4.497

2.  Improving condition severity classification with an efficient active learning based framework.

Authors:  Nir Nissim; Mary Regina Boland; Nicholas P Tatonetti; Yuval Elovici; George Hripcsak; Yuval Shahar; Robert Moskovitch
Journal:  J Biomed Inform       Date:  2016-03-22       Impact factor: 6.317

3.  Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

Authors:  Nir Nissim; Yuval Shahar; Yuval Elovici; George Hripcsak; Robert Moskovitch
Journal:  Artif Intell Med       Date:  2017-04-27       Impact factor: 5.326

4.  Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection.

Authors:  Robert Moskovitch; Hyunmi Choi; George Hripcsak; Nicholas Tatonetti
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2016-07-14       Impact factor: 3.710

  4 in total

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