Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management - Computational Intelligence in Healthcare: Advanced Methodologies - page 158

Table 1. CBR in medicine, examples of some systems and their application domain/context

No Author/system Application domain/context

1 De Paz/ExpressionCBR [46] Cancer diagnosis

2 Perner/Fungi-PAD [47], [48] Object recognition

3 Cordier/FrakaS [49] Oncology

4 Corchado/GerAmi [50] Alzheimer patients

5 Glez-Peña/geneCBR [51], [52] Cancer classification

6 Perner/HEp2-PAD[53], [54],

[55]

Image classifier

7 Schmidt/ISOR [56] Endocrine

8 D'Aquin/KASIMIR [57] Breast cancer

9 Bichindaritz/Mémoire [58] Biology & medicine

10 Montani/RHENE [59] Hemodialysis

11 Kwiatkowska/Somnus [60] Obstructive sleep apnea

12 Lorenzi/SISAIH [61] Fraud detection in health care

13 Ochoa /SIDSTOU [62] Tourette syndrome

14 Brien/ADHD [63] Neuropsychiatric

15 Doyle/Bronchiolitis [64] Bronchiolitis

16 O'Sullivan/Dermatology [65] Dermatology

17 Marling/Type-1diabetes [66] Diabetes

18 Song/radiotherapy planning

[67]

Prostate cancer

retrieved and ranked cases in order to develop a solution to the problem in a new case.
The clinician/expert determines if it is a plausible solution to the problem and makes
modifications to the solution. The case is then sent to the revision step where the
solution is verified manually for correctness and is presented as a confirmed solution
to the new problem case. In the medical system, there is not much adaptation, espe-
cially in a decision support system where the best cases are proposed to the
clinician as suggestions of solutions and when the domain knowledge is not clear
enough [12].
Retain : Finally, this new solved case is added to the case base functioning as a
learning process in the CBR cycle and allows the user to solve a future problem by
using this solved case. Retaining of a new solved case could be done manually based
on the clinician or expert's decision.
CBR in Medicine. The origin of CBR stems from the work of Schank and Abelson in
1977 [25] at Yale University. According to Schank [26], “remembering is at the root
of how we understand... at the root of how we learn.” They have explored that new