Abstract
Breast cancer is identified as the most common type of cancer in women worldwide with 1.6 million women around the world diagnosed every year. This prompts many active areas of research in identifying better ways to prevent, detect, and treat breast cancer. DESIREE is a European Union funded project, which aims at developing a web-based software ecosystem for the multidisciplinary management of primary breast cancer. The development of an intelligent clinical decision support system offering various modalities of decision support is one of the key objectives of the project. This paper explores case-based reasoning as a problem solving paradigm and discusses the use of an explicit domain knowledge ontology in the development of a knowledge-intensive case-based decision support system for breast cancer management.
Highlights
Breast cancer is the most common type of cancer in women worldwide, with the mortality rate being second highest among different types of cancer.[1]
The survival rates vary from 40%, 60%, to 80% in low-income, middle-income, and developed countries respectively, which reflects the lack of early detection programs and adequate diagnosis and treatment facilities in the less-developed countries
We present the knowledge intensive case-based reasoning (KI-Case-based Reasoning (CBR)) model, which incorporates knowledge from explicit domain knowledge we developed and serves as the case-based decision support system (DSS) (CB-DSS) within the DESIREE project
Summary
Breast cancer is the most common type of cancer in women worldwide, with the mortality rate being second highest (next to lung cancer) among different types of cancer.[1]. As the clinical decisions made by physician improves with experience, the performance of CBR model would improve with usage, which is not possible in a rule-based model.[11] the fact that the CBR methodology closely resembles the thought process of the clinicians, and the gradual acceptance of advanced decision support systems in clinical practice, suggest the success of CBR in medicine.[12] Some of the CBR systems developed in medicine and health science domains so far, include CASEY13 to diagnose heart failure patients, MNAOMIA14 to diagnose and treat eating disorders, PROTOS15 for hearing disorder diagnosis, MacRad[16] for radiology image classification, GerAmi[17] for Alzheimer’s disease management, and GOCBR18 for breast cancer diagnosis Many such CBR applications are well summarized by Choudhury et al.[11].
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