Literature DB >> 11604798

Building an explanation function for a hypertension decision-support system.

R D Shankar1, S B Martins, S W Tu, M K Goldstein, M A Musen.   

Abstract

ATHENA DSS is a decision-support system that provides recommendations for managing hypertension in primary care. ATHENA DSS is built on a component-based architecture called EON. User acceptance of a system like this one depends partly on how well the system explains its reasoning and justifies its conclusions. We addressed this issue by adapting WOZ, a declarative explanation framework, to build an explanation function for ATHENA DSS. ATHENA DSS is built based on a component-based architecture called EON. The explanation function obtains its information by tapping into EON's components, as well as into other relevant sources such as the guideline document and medical literature. It uses an argument model to identify the pieces of information that constitute an explanation, and employs a set of visual clients to display that explanation. By incorporating varied information sources, by mirroring naturally occurring medical arguments and by utilizing graphic visualizations, ATHENA DSS's explanation function generates rich, evidence-based explanations.

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Mesh:

Year:  2001        PMID: 11604798

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


  4 in total

1.  Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers.

Authors:  Mary K Goldstein; Robert W Coleman; Samson W Tu; Ravi D Shankar; Martin J O'Connor; Mark A Musen; Susana B Martins; Philip W Lavori; Michael G Shlipak; Eugene Oddone; Aneel A Advani; Parisa Gholami; Brian B Hoffman
Journal:  J Am Med Inform Assoc       Date:  2004-06-07       Impact factor: 4.497

2.  Use of declarative statements in creating and maintaining computer-interpretable knowledge bases for guideline-based care.

Authors:  Samson W Tu; Karen M Hrabak; James R Campbell; Julie Glasgow; Mark A Nyman; Robert McClure; James McClay; Robert Abarbanel; James G Mansfield; Susana M Martins; Mary K Goldstein; Mark A Musen
Journal:  AMIA Annu Symp Proc       Date:  2006

3.  An Algorithm Using Twelve Properties of Antibiotics to Find the Recommended Antibiotics, as in CPGs.

Authors:  R Tsopra; A Venot; C Duclos
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

Review 4.  Using health information technology to improve hypertension management.

Authors:  Mary K Goldstein
Journal:  Curr Hypertens Rep       Date:  2008-06       Impact factor: 5.369

  4 in total

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