NDLI: Architecture-level dependability analysis of a medical decision support system

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Content Provider	ACM Digital Library
Author	Beckerman, Barbara G. Pullum, Laura L. Symons, Christopher T. Patton, Robert M.
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.
Starting Page	83
Ending Page	88
Page Count	6
File Format	PDF
ISBN	9781605589732
DOI	10.1145/1809085.1809096
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-05-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Medical decision support Failure modes Mammography reports Architecture-level analysis Mammography images
Content Type	Text
Resource Type	Article

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Architecture-level dependability analysis of a medical decision support system