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The Application of Data Mining in Clinic Laboratory Information System Based on Association Rules and Clustering

Author: SuKai
Tutor: LiWeiPeng
School: First Military Medical University
Course: Biomedical Engineering
Keywords: Association rules Apriori algorithm Cluster analysis EM algorithm Tolerance test Blood cell count
CLC: R319
Type: Master's thesis
Year: 2005
Downloads: 248
Quote: 2
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Abstract


Clinical Laboratory Information System (Clinic Laboratory Information System, CLIS) is an important part of the hospital information system . It is based on a database , seamless network and computer software and instrument connected to analog Laboratory workflow , electronic inspection information and laboratory information management automation . Clinical laboratory information system applications generate a large amount of data , data mining methods to analyze these data mining to discover useful knowledge , it is necessary to carry out the work . There are still no reports in this regard , the international community at the exploratory stage . In this paper, a glucose tolerance test and blood count two test data , the whole process of detailed laboratory information system based on data pre-processing , data mining and knowledge discovery . And focuses on the application of the association rule mining and clustering analysis of the two data mining methods in practice . This article focuses on the tolerance test data preprocessing , association rule mining application in the diagnosis of diabetes . Tolerance test data from the basic information of the patients in the hospital information system , the oral glucose tolerance test data extracted from the laboratory information system , as well as other auxiliary information obtained from some questionnaire . In filling the vacancy value of qualitative data quantization process attribute range transform , unified dimensionless , and delete unrelated isolated SQLSERVER DTS introduced into the data warehouse , data integration platform SQLSERVER ANALYSIS MANAGER multidimensional construct data cube . To improve the efficiency of data mining , data attributes for the theme dimensional Statute exclude irrelevant attributes relevant attributes reserved . Dimension reduction greedy algorithm , and gradually move to select the useful properties . Calculate the entropy gain values ??used in the correlation analysis of the properties of the entropy gain is set to the minimum information gain , after the introduction of a new attribute , and the minimum information gain threshold comparing to determine the usefulness of the attribute . Association rule mining is one of the important applications in data mining . In this paper, the classic Apriori algorithm , and the support and credibility on the basis of the concept of the original association rules , and introduced the concept of the degree of interest

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