Literature DB >> 17911927

Intelligent querying and exploration of multiple time-oriented medical records.

Denis Klimov1, Yuval Shahar.   

Abstract

Querying and analyzing multiple time-oriented patient data is a key task during medical research, clinical trials or the assessment of the quality of therapy. In this paper, we present several aspects of the VISITORS system, which includes knowledge-based tools for graphical querying and exploration of multiple longitudinal patient records. We focus on the syntax and semantics of the knowledge-based aggregation query language for multiple time-oriented patient records, and on the graphical query-construction interface. The query language assumes an underlying computational method for deriving meaningful abstractions from single and multiple patient records, such as we had previously developed. The aggregation query language enables population querying using an expressive set of constraints. By using our underlying temporal mediator architecture, the time needed to answer typical temporal-abstraction aggregation queries on databases of 1000 to 10,000 patients was reasonable.

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Year:  2007        PMID: 17911927

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


  4 in total

1.  Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data.

Authors:  Susana B Martins; Yuval Shahar; Dina Goren-Bar; Maya Galperin; Herbert Kaizer; Lawrence V Basso; Deborah McNaughton; Mary K Goldstein
Journal:  Artif Intell Med       Date:  2008-04-28       Impact factor: 5.326

2.  Modeling temporal relationships in large scale clinical associations.

Authors:  David A Hanauer; Naren Ramakrishnan
Journal:  J Am Med Inform Assoc       Date:  2012-09-27       Impact factor: 4.497

3.  Composite patient reports: a laboratory informatics perspective and pilot project for personalized medicine and translational research.

Authors:  Adi V Gundlapalli; Julio C Delgado; Brian R Jackson; Guido J Tricot; Harry R Hill
Journal:  Summit Transl Bioinform       Date:  2009-03-01

4.  Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks.

Authors:  Maxwell Salvatore; Lauren J Beesley; Lars G Fritsche; David Hanauer; Xu Shi; Alison M Mondul; Celeste Leigh Pearce; Bhramar Mukherjee
Journal:  J Biomed Inform       Date:  2020-12-03       Impact factor: 8.000

  4 in total

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