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Application of Data Mining Techniques to Healthcare Data

Published online by Cambridge University Press:  02 January 2015

Mary K. Obenshain
Mary K. Obenshain*
Affiliation:
North Carolina
*
Data Quality Research Institute, UNC at Chapel Hill, CB#7226, 200 Timberhill Place, Suite 201, Chapel Hill, NC 27599-7226
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Abstract

A high-level introduction to data mining as it relates to surveillance of healthcare data is presented. Data mining is compared with traditional statistics, some advantages of automated data systems are identified, and some data mining strategies and algorithms are described. A concrete example illustrates steps involved in the data mining process, and three successful data mining applications in the healthcare arena are described.

Type
Statistics for Hospital Epidemiology
Information
Infection Control & Hospital Epidemiology , Volume 25 , Issue 8 , August 2004 , pp. 690 - 695
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2004

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References

1.Birnbaum, D. Analysis of hospital infection surveillance data. Infect Control 1984;5:332–338.Google Scholar
2.Sellick, JA JrThe use of statistical process control charts in hospital epidemiology. Infect Control Hosp Epidemiol 1993;14:649–656.CrossRefGoogle ScholarPubMed
3.Finison, LJ, Spencer, M, Finison, KS. Total quality measurement in health care: using individuals charts in infection control. ASQC Quality Congress Transactions 1993;47:349–359.Google Scholar
4.Ngo, L, Tager, IB, Hadley, D. Application of exponential smoothing for nosocomial infection surveillance. Am J Epidemiol 1996;143:637–647.CrossRefGoogle ScholarPubMed
5.Benneyan, JC. Statistical quality control methods in infection control and hospital epidemiology (parts I and II). Infect Control Hosp Epidemiol 1998;19:194-214, 265–283.CrossRefGoogle Scholar
6.Kaminsky, FC, Benneyan, JC, Davis, RD, et al.Statistical control charts based on a geometric distribution. Journal of Quality Technology 1992;24:63–69.Google Scholar
7.Gustafson, TL. Practical risk-adjusted quality control charts for infection control. Am J Infect Control 2000;28:406–414.Google Scholar
8.Benneyan, JC. Number-between g-type statistical quality control charts for monitoring adverse events. Health Care Manag Sci 2001;4:305–318.Google Scholar
9.Johnston, G. System adds to biodefense readiness. Bio-IT World. November 1, 2002. Available at www.bio-itworld.com/news/110102_reportl436.html. Accessed July 21, 2004.Google Scholar
10.Matkovsky, IP, Nauta, KR. Overview of data mining techniques. Presented at the Federal Database Colloquium and Exposition; September 9-11, 1998; San Diego, CA.Google Scholar
11.Lajiness, MS. Using Enterprise Miner to explore and exploit drug discovery data. Proceedings from the 25th Annual SAS User Group International; April 9-12, 2000; Indianapolis, IN.Google Scholar
12.Perleman, RS, Smith, KM. Novel software tools for chemical diversity. Perspectives in Drug Discovery & Design 1998;9:339–353.CrossRefGoogle Scholar
13.Mostashari, F, Kulldorff, M, Hartman, J, Miller, J, Kulasekera, V. Dead bird clusters as an early warning system for West Nile virus activity. Emerg Infect Dis 2003;9:641–646.Google Scholar
14.Gaynes, R, Richards, C, Edwards, J, et al.Feeding back surveillance data to prevent hospital-acquired infections. Emerg Infect Dis 2001;7:295–298.Google Scholar
15.Brosette, SE, Spragre, AP, Jones, WT, Moser, SA. A data mining system for infection control surveillance. Methods Inf Med 2000;39:303–310.Google Scholar
16.Cerrito, P. Using text analysis to examine ICD-9 codes to determine uniformity in the reporting of MedPAR data. Presented at the Annual Symposium of the American Medical Informatics Association; November 9-13, 2002; San Antonio, TX.Google Scholar
17.Ridinger, M. American Healthways uses SAS to improve patient care. DM Review 2002;12:139.Google Scholar