- Collaborative Case-Based Reasoning for Knowledge Discovery of Elders Health Assessment System
- Jurnal Ilmiah Teknologi Informasi Terapan
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- Foundations of Soft Case-Based Reasoning
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The core of the optimal matching retrieval is similarity calculation. Assume Domain C is the set of all solution cases in the case library, c 0 is inputted problem case to be solved, C is the set of candidate cases from the preliminaryed, is the set of candidate cases from the preliminary retrieval.
Formula 5 determines the gray correlation coefficient matrix, but each characteristic attribute does not have the same importance degree; therefore, characteristic attribute weights must be taken into account. Combined with the specific operating characteristics of the system, Analytic Hierarchy Process is used to determine the normalized characteristic attribute weight vector. The generalized weighted distance between the problem case c 0 and the solution case c i. According to the physical meaning of Formula 7 , the larger s i m 0 i value is, the more similar the corresponding case is with the problem case c 0.
Another way is to rank similar cases in light of their similarities, then select the optimal matching case from the top-ranking cases according to the elders health needs. It is necessary to note that to effectively retrieve, there is need to determine the initial weights of case attributes at first. In the retrieval process, if the weights of case characteristic attributes can not be correctly valued, the case retrieval quality will not be guaranteed, and then there is need to adjust and optimize the weights.
In EHA retrieval, here classic Delphi method is used to determine the initial weights. In the retrieval process, Tabu Retrieval Genetic Algorithm proposed in Bibliography [ 13 ] is used to optimize the weights of case characteristic attributes. Self-adaptation and memory function of Tabu Retrieval Genetic Algorithm are used to improve the global retrieval ability and convergence speed of Tabu Retrieval Genetic Algorithm.
In this way, the attribute weights can be automatically optimized to improve case retrieval accuracy.
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Because the knowledge involved in the case revision is mostly the tacit expert knowledge, the complete revision automatically made by the system will be very difficult. The revised assessment program is integrated with the previous description of the problem case, and a new case is reproduced and evaluated whether it is worth saving or not.
If there is need to save, the new case is saved as new knowledge in the case library to expand problem solving capacity. In this way, the case study is completed. Then according to main characteristic attributes, calculate the similarity between the candidate case set and the problem case to get the solution case number.
From the most similar case number, get the corresponding EHA program. By learning, it is easy to extract related knowledge from the knowledge library or the experts who have the knowledge to form the assessment program, then modify and optimize the EHA solution scheme with the knowledge in the knowledge library. Re-examine and evaluate the optimized case to see whether it is worth saving or not.
Collaborative Case-Based Reasoning for Knowledge Discovery of Elders Health Assessment System
If it is worthy saving, the optimized case needs to be added into the case library as a new case. The initial threshold value and the initial weights can be obtained with Delphi method by domain experts. To make the expert advice more reasonable and more suitable for the system and the experimental requirements, to get the optimal initial threshold value and the initial weights, a training library is added up to Case Base-EHA system design, in which there are programs to let the experts operate.
In Case Base-EHA system, the experts use the data in the training library to have repeated experiments, and then give the threshold value and weights. Next, EHA programs are used as experiment cases. As the initial case library, 20, 40, 60, 80, , cases are respectively put in six sub-systems of Case Base-EHA system to have the retrieval experiment for a hospital EHA program. Each characteristic attribute value is inputted to have the retrieval. The retrieved results of six sub-case libraries and the logarithmic graph trend are shown in Fig.
With the increase of the data in the case library, the retrieval accuracy is gradually increased the retrieved maximum similarity value is monotonically increasing , the overall retrieval time is short and remains relatively stable though the data is constantly increasing, which reflects good retrieval efficiency.
Jurnal Ilmiah Teknologi Informasi Terapan
The community program is integrated with Programs and , and mildly modified. The community EHA program is obtained in a relatively short time, which greatly reduces the assessment time. Specially, we are grateful to the senior editor, associate editor, and the anonymous reviewers whose comments have improved this paper considerably.
This paper applies multi-case-based reasoning in EHA and knowledge reuse, puts forward the application framework of EHA knowledge reuse system, investigates case representation, retrieval, optimization and correction, reuse and other key techniques. Because case representation is the basis of CCBR, this paper uses XML-based multi-case semi-structured knowledge representation to describe and organize cases, and proposes specific XML-based case-oriented approach.
On this basis, this paper uses Knowledge-Guided Approach with Nearest-Neighbor to retrieve and match cases.
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In light of the complexity of EHA, Gray Relational Analysis with weighted Euclidean Distance is used to calculate similarity; thus, the quality of multi-case retrieval is guaranteed. Finally the application is introduced. As a preliminary research result, this material has some problems, for example, the prototype system is too simple, the data is small and etc,. Future researches will focus on developing well-functioned CCBR-EHA experimental system and make the retrieval experiment with massive data.
The authors confirm that this article content has no conflict of interest. National Center for Biotechnology Information , U.
Foundations of Soft Case-Based Reasoning
Open Biomed Eng J. Published online Sep China Find articles by Ping Hu. China Find articles by Dong-xiao Gu. China Find articles by Yu Zhu.
Author information Article notes Copyright and License information Disclaimer. China; Tel: ; Fax; E-mail: nc. Abstract The existing Elders Health Assessment EHA system based on single-case-library reasoning has low intelligence level, poor coordination, and limited capabilities of assessment decision support. Keywords: Elders health assessment system, knowledge discovery, knowledge mining, multi-case-based reasoning.
The process keeps to the following steps: Step 1: According to the actual health status of the old person and knowledge needs, define characteristic attributes of a new problem case; Step 2: Retrieve the case library according to feature attributes, and with CBR and similarity analysis determine the case that is the most similar to the new problem case; Step 3: Reuse the assessment program that is corresponding to the most similar case as the feasible assessment program; Step 4: Modify and optimize the assessment program, and examine whether it meets the needs of the new case.
If it does not meet the needs, continue to modify and optimize to make it more suitable to the new case; Step 5: Reproduce the assessment program that meets the requirements and put it into the new case to perfect the new case; Step 6: Re-examine and evaluate whether the new case is worth saving. If necessary, save it in the case library, and retain the corresponding assessment program; Step 7: Modify the case indexing and feature weights in the case library. Multi-case Representation Case representation is the basic work in the knowledge reuse process of EHA, and its representing method is directly related to the efficiency and accuracy of CBR.
Multi-cases Knowledge Discovery Case retrieval model and its retrieval strategy have significant impacts on CBR system learning and reasoning performance, whose essence is the similarity matching of the problem case and the solution case [ 14 ]. Open in a separate window. Optimal Case Acquisition The core of the optimal matching retrieval is similarity calculation. Table 1. Patient Educ.
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