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Data Mining Techniques for Assisting the Diagnosis of Pressure Ulcer Development in Surgical Patients.
MedLine Citation:
PMID:  21503743     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Pressure ulcer is a serious problem during patient care processes. The high risk factors in the development of pressure ulcer remain unclear during long surgery. Moreover, past preventive policies are hard to implement in a busy operation room. The objective of this study is to use data mining techniques to construct the prediction model for pressure ulcers. Four data mining techniques, namely, Mahalanobis Taguchi System (MTS), Support Vector Machines (SVMs), decision tree (DT), and logistic regression (LR), are used to select the important attributes from the data to predict the incidence of pressure ulcers. Measurements of sensitivity, specificity, F(1), and g-means were used to compare the performance of four classifiers on the pressure ulcer data set. The results show that data mining techniques obtain good results in predicting the incidence of pressure ulcer. We can conclude that data mining techniques can help identify the important factors and provide a feasible model to predict pressure ulcer development.
Authors:
Chao-Ton Su; Pa-Chun Wang; Yan-Cheng Chen; Li-Fei Chen
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-4-19
Journal Detail:
Title:  Journal of medical systems     Volume:  -     ISSN:  0148-5598     ISO Abbreviation:  -     Publication Date:  2011 Apr 
Date Detail:
Created Date:  2011-4-19     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7806056     Medline TA:  J Med Syst     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Room 820, Engineering Building I, 101, Sec. 2, Kuang Fu Rd., Hsinchu, 30013, Taiwan, Republic of China, ctsu@mx.nthu.edu.tw.
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