Architecture-Level Dependability Analysis of a Medical Decision Support System

Conference · Fri Jan 01 00:00:00 EST 2010

OSTI ID:981430

Pullum, Laura L ^[1]; Symons, Christopher T ^[1]; Patton, Robert M ^[1]; Beckerman, Barbara G ^[1]

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1. ORNL

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.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 981430
• Resource Relation: Conference: Software Engineering in Health Care (workshop associated with Intl Conf on Software Engineering), Cape Town, South Africa, 20100503, 20100508
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
DESIGN
ENGINEERING
EVALUATION
FAILURES
IMAGES
LEARNING
ORDERS
PATIENTS
PERFORMANCE
POTENTIALS