Application of data mining techniques to healthcare data

Infection Control and Hospital Epidemiology

Volumn 25, Issue 8, 2004, Pages 690-695

  Obenshain, Mary K ^a,b  

^a CB#7226 ^*

^b UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; COMPUTER PROGRAM; DATA BASE; HEALTH CARE; HIGH RISK PATIENT; HOSPITAL INFECTION; HUMAN; INFECTION CONTROL; INFORMATION PROCESSING; PREDICTION; PROBABILITY; REVIEW; SAMPLING; STATISTICAL ANALYSIS; STATISTICAL MODEL;

DATABASES, FACTUAL; DELIVERY OF HEALTH CARE; HOSPITALS; HUMANS; INFECTION CONTROL; INFORMATION STORAGE AND RETRIEVAL; POPULATION SURVEILLANCE; SOFTWARE;

EID: 4143096884     PISSN: 0899823X     EISSN: None     Source Type: Journal    
DOI: 10.1086/502460     Document Type: Review

References (17)

1. Birnbaum D. Analysis of hospital infection surveillance data. Infect Control 1984;5:332-338.
2. Sellick JA Jr. The use of statistical process control charts in hospital epidemiology. Infect Control Hosp Epidemiol 1993;14:649-656.
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.
4. Ngo L, Tager IB, Hadley D. Application of exponential smoothing for nosocomial infection surveillance. Am J Epidemiol 1996;143:637-647.
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.
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.
7. Gustafson TL. Practical risk-adjusted quality control charts for infection control. Am J Infect Control 2000;28:406-414.
8. Benneyan JC. Number-between g-type statistical quality control charts for monitoring adverse events. Health Care Manag Sci 2001;4:305-318.
9. Johnston G. System adds to biodefense readiness. Bio-IT World. November 1, 2002. Available at www.bio-itworld.com/news/110102_report1436.html. Accessed July 21, 2004.
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.
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.
12. Perleman RS, Smith KM. Novel software tools for chemical diversity. Perspectives in Drug Discovery & Design 1998;9:339-353.
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.
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.
15. Brosette SE, Spragre AP, Jones WT, Moser SA. A data mining system for infection control surveillance. Methods Inf Med 2000;39:303-310.
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.
17. Ridinger M. American Healthways uses SAS to improve patient care. DM Review 2002;12:139.