Literature DB >> 11552344

RAGs: A novel approach to computerized genetic risk assessment and decision support from pedigrees.

A S Coulson1, D W Glasspool, J Fox, J Emery.   

Abstract

OBJECTIVES: To assist general practitioners in evaluating patients' genetic risk of cancer on the basis of family history data.
METHODS: A new computer application, RAGs (Risk Assessment in Genetics), has been developed to help doctors create graphical family trees and assess the genetic risk of breast and colorectal cancer. RAGs possesses two features that distinguish it from similar software: (i) a user-centred design, which takes into account the requirements of the doctor-patient encounter; (ii) effective and accessible risk reporting by employing qualitative evidence for or against increased risk, which is more easily understood than numerical probabilities. The system allows any rule-based genetic risk guideline to be implemented, and may be readily modified to cater for the varying degrees of information required by different specialists.
RESULTS: RAGs permits fast, accurate data entry, and results in more appropriate management decisions than those made via other techniques. In addition, RAGs enables both the clinician and the patient to understand how it arrives at its conclusions, since the use of qualitative evidence allows the program to provide explanations for its reasoning.
CONCLUSIONS: The RAGs system promises to help practitioners be more effective gatekeepers to genetic services. It may empower doctors both to make an informed choice when deciding to refer patients who are at increased genetic risk of breast or colorectal cancer, and to reassure those who are at low risk.

Entities:  

Mesh:

Year:  2001        PMID: 11552344

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  6 in total

1.  A generic computerized method for estimate of familial risks.

Authors:  Isabelle Colombet; Yigang Xu; Marie-Christine Jaulent; Daniel Desages; Patrice Degoulet; Gilles Chatellier
Journal:  Proc AMIA Symp       Date:  2002

2.  The syntax and semantics of the PROforma guideline modeling language.

Authors:  David R Sutton; John Fox
Journal:  J Am Med Inform Assoc       Date:  2003-06-04       Impact factor: 4.497

3.  From practice guidelines to clinical decision support: closing the loop.

Authors:  John Fox; Vivek Patkar; Ioannis Chronakis; Richard Begent
Journal:  J R Soc Med       Date:  2009-11       Impact factor: 5.344

Review 4.  Clinical decision support for genetically guided personalized medicine: a systematic review.

Authors:  Brandon M Welch; Kensaku Kawamoto
Journal:  J Am Med Inform Assoc       Date:  2012-08-25       Impact factor: 4.497

5.  Towards the use of argumentation in bioinformatics: a gene expression case study.

Authors:  Kenneth McLeod; Albert Burger
Journal:  Bioinformatics       Date:  2008-07-01       Impact factor: 6.937

Review 6.  Clinical prediction rules in practice: review of clinical guidelines and survey of GPs.

Authors:  Annette Plüddemann; Emma Wallace; Clare Bankhead; Claire Keogh; Danielle Van der Windt; Daniel Lasserson; Rose Galvin; Ivan Moschetti; Karen Kearley; Kirsty O'Brien; Sharon Sanders; Susan Mallett; Uriell Malanda; Matthew Thompson; Tom Fahey; Richard Stevens
Journal:  Br J Gen Pract       Date:  2014-04       Impact factor: 5.386

  6 in total

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