Architecture-level dependability analysis of a medical decision support system

Laura L. Pullum, Christopher T. Symons, Robert M. Patton, Barbara G. Beckerman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Recent advances in techniques such as image analysis, text analysis and machine learning have shown great potential to assist physicians in detecting and diagnosing health issues in patients. In this paper, we describe the approach and findings of an architecture-level dependability analysis for a mammography decision support system that incorporates these techniques. The goal of the research described in this paper is to provide an initial understanding of the dependability issues, particularly the potential failure modes and severity, in order to identify areas of potential high risk. The results will guide design decisions and provide the basis of a dependability and performance evaluation program.

Original languageEnglish
Title of host publication2010 ICSE Workshop on Software Engineering in Health Care, SEHC 2010, in Conjunction with the 32nd ACM/IEEE International Conference on Software Engineering, ICSE 2010
Pages83-88
Number of pages6
DOIs
StatePublished - 2010
Event2010 ICSE Workshop on Software Engineering in Health Care, SEHC 2010, in Conjunction with the 32nd ACM/IEEE International Conference on Software Engineering, ICSE 2010 - Cape Town, South Africa
Duration: May 2 2010May 8 2010

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257

Conference

Conference2010 ICSE Workshop on Software Engineering in Health Care, SEHC 2010, in Conjunction with the 32nd ACM/IEEE International Conference on Software Engineering, ICSE 2010
Country/TerritorySouth Africa
CityCape Town
Period05/2/1005/8/10

Keywords

  • Architecture-level analysis
  • Failure modes
  • Mammography images
  • Mammography reports
  • Medical decision support

Fingerprint

Dive into the research topics of 'Architecture-level dependability analysis of a medical decision support system'. Together they form a unique fingerprint.

Cite this