Home ยป Case based reasoning in data mining

Case based reasoning in data mining

CB can hence be viewed as a methodology able to combine reasoning and learning steps and to produce solutions to problems by taking into account past experience. The use of CB technologies is particularly appealing in health care, where the capability of intelligently retrieving similar situations in a data-base of past cases can be a useful instrument for maintaining and improving the “intellectual capital” of the hospital institution over years.

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In several application contexts, and in particular in chronic patients’ management, a large amount of data is collected for a long time; in this situation, CB may considerably decrease the probability of making the same wrong decision in similar situations, even when the decision-maker is different different physicians in the same or in different hospitals). In this paper we will describe an application of case-based retrieval techniques in the context of clinical monitoring of institutionalized diabetic patients. In current medical practice, diabetic patients are visited several times a year.

During these visits the patient condition is assessed by analyzing the self-monitoring data; the therapeutic protocol is then revised. In the control visits, physicians are interested in detecting if the same situation has already been experienced by the patient or by a animal patient, and, in that case, in 1 Divertimento did Information e Systematic, Fermata 1, 1-27100 Via, Italy, [email protected] Unpin. It, [email protected] Unpin. It, [email protected] Unpin. It 2 Divertimento did Information, University’ did Torsion, Coors Civilizes 185, la 10149 Torsion, Italy, [email protected] Unit. It 3 1. R. C. C. S. Polyclinics S.

Mateo, P. El Googol 2, 1-27100 PTA, Italy, [email protected] Nntp. It seeing what decision has been taken and what was the outcome of that decision. This kind of support may be particularly interesting, considering that each topologists may visit more than a hundred patients every month; in this context, peeping track of the history of all patients is quite complex. For these reasons, our proposed tool was designed to select in the data-base past cases that are in some sense similar to the current one; the result of the search is shown to physicians for comparison and further adaptation to the current case.

A crucial capability of the proposed tool is the possibility of analyzing the overall history related with the retrieved cases, showing the sequence of decision steps that precede and follow the retrieved situation. The paper is structured as follows: Section 2 presents, from a methodological point of IEEE, the peculiarities of the CB system herein proposed and its relationships with IDA. Section 3 deals with a short description of the application domain, while Section 4 and 5 describe in detail the current implementation of the system.

Section 6 presents some results obtained on a data-base of 100 cases collected at the Polyclinic S. Mateo Hospital of Via. Finally, Section 7 addresses some conclusions and some directions for future work. 2 CB AND IDA: THE SYSTEM DESIGN Several medical applications may require the improvement of classical CB solutions, in order to explicitly take into account the prior domain knowledge. In fact, the “intelligence” of a CB system resides in its case base, since the knowledge is implicit in cases [4].

While classical approaches restrict the role of the prior knowledge in coping with the task of features selection, a further step is to encode background knowledge as a set of prototypical cases. Such knowledge may be helpful in all phases of the CB process, from feature selection to adaptation. In particular, case retrieval, the most computationally expensive step of the method, can gain in efficiency from a knowledge-based structuring of the case-base. Rather interestingly, ease retrieval involves different subset’s that are related with an intelligent analysis of the available data 4 such steps may be summarized ; as follows [1]: 1 . Tuition assessment, to elaborate case description and to clarify the relevant context to work with; 2. Case memory search, to retrieve partially matching cases; 3. Best case selection. In biomedical applications, situation assessment may be exploited to identify the contextual framework on which the actual retrieval has to take place; therefore, this step may be very critical in order to focus the search of the case library on relevant an be useful for decision making. CB can be viewed as a generalization of Instance-Based learning [7] Situation assessment and case search are strongly influenced by the organization structures on which the case memory is based on. Such structures range from flat memories where cases are stored sequentially in a very simple way, such as in lists or feature vectors, to more complex organizations based on graphs and trees like shared features nets and discrimination nets [2].

Obviously a trade off is present between memory structure maintenance and computational cost of retrieval; in fact, hill simple structures like those used in flat memory organizations are easy to update, more complex structures may be severely impacted in their organization where an update takes place. In our application we have implemented a method to make case retrieval more flexible, by structuring the case memory through the partitioning induced by a set of prototypical classes.

A taxonomy representing such classes is assumed to be available and cases are suitably associated with each basic class. Background knowledge is hence exploited to define a retrieval strategy in which situation assessment step is obtained through a classification procedure, that associates the current case to a class in the taxonomy. In more detail, we consider a tree-structured taxonomic knowledge. Each class in the hierarchy is a prototypical description of the set of problems or situations it summarizes and is connected to classes representing its specializations through sub-class links.


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