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Data Mining and Clinical Decision Support Systems

  • Chapter
Clinical Decision Support Systems

Part of the book series: Health Informatics ((HI))

Abstract

Data mining is a process of pattern and relationship discovery within large sets of data. The context encompasses several fields, including pattern recognition, statistics, computer science, and database management. Thus the definition of data mining largely depends on the point of view of the writer giving the definitions. For example, from the perspective of pattern recognition, data mining is defined as the process of identifying valid, novel, and easily understood patterns within the data set.1

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References

  1. Fayyad UM, Piatetsky-Shapiro G, Smyth P. Knowledge discovery and data mining: towards a unifying framework. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 82–88. Portland, Oregon, August 1996. AAAI Press. Available from: http://ww-aig.jpl.nasa.gov. kdd96. Accessed July 17, 2006.

    Google Scholar 

  2. Leatherman S, Peterson E, Heinen L, Quam L. Quality screening and management using claims data in a managed care setting. QRB Qual Rev Bull 1991; 17:349–359.

    PubMed  CAS  Google Scholar 

  3. Duda RO, Hart PE, Stork DG. Pattern classification and scene analysis, 2nd ed. New York: John Wiley and Sons; 2000.

    Google Scholar 

  4. Fukunaga K. Introduction to statistical pattern recognition, 2nd ed. New York: Academic Press; 1990.

    Google Scholar 

  5. Schalkoff RJ. Pattern recognition: statistical, structural and neural approaches. New York: John Wiley and Sons; 1991.

    Google Scholar 

  6. Finlay PN. Introducing decision support systems. Cambridge, MA: Blackwell Publishers; 1994.

    Google Scholar 

  7. Marakas GM. Decision support systems, 2nd ed. Princeton, NJ: Prentice Hall; 2002.

    Google Scholar 

  8. Ambrosiadou BV, Goulis DG, Pappas C. Clinical evaluation of the DIABETES expert system for decision support by multiple regimen insulin dose adjustment. Comp Methods Programs Biomed 1996;49:105–115.

    Article  CAS  Google Scholar 

  9. Marchevsky AM, Coons G. Expert systems as an aid for the pathologist’s role of clinical consultant: CANCER-STAGE. Mod Pathol 1993;6:265–269.

    PubMed  CAS  Google Scholar 

  10. Nguyen AN, Hartwell EA, Milam JD. A rule-based expert system for laboratory diagnosis of hemoglobin disorders. Arch Pathol Lab Med 1996;120:817–827.

    PubMed  CAS  Google Scholar 

  11. Papaloukas C, Fotiadis DI, Likas A, Stroumbis CS, Michalis LK. Use of a novel rule-based expert system in the detection of changes in the ST segment and the T wave in long duration ECGs. J Electrocardiol 2002;35:27–34.

    Article  PubMed  Google Scholar 

  12. Riss PA, Koelbl H, Reinthaller A, Deutinger J. Development and application of simple expert systems in obstetrics and gynecology. J Perinat Med 1988;16: 283–287.

    Article  PubMed  CAS  Google Scholar 

  13. Sailors RM, East TD. A model-based simulator for testing rule-based decision support systems for mechanical ventilation of ARDS patients. Proc Ann Symp Comp Appl Med Care 1994:1007.

    Google Scholar 

  14. Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res 1975; 8:303–320.

    Article  PubMed  CAS  Google Scholar 

  15. Goldman L, Cook EF, Brand DA, et al. A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med 1988;318:797–803.

    Article  PubMed  CAS  Google Scholar 

  16. Scott AJ, Wild CJ. Fitting logistic models under case-control or choice based sampling. J Roy Stat Soc B 1986;48:170–182.

    Google Scholar 

  17. Avanzolini G, Barbini P, Gnudi G. Unsupervised learning and discriminant analysis applied to identification of high risk postoperative cardiac patients. Int J Biomed Comp 1990;25:207–221.

    Article  CAS  Google Scholar 

  18. Gerald LB, Tang S, Bruce F, et al. A decision tree for tuberculosis contact investigation [see comment]. Am J Respir Crit Care Med 2002;166:1122–1127.

    Article  PubMed  Google Scholar 

  19. Wang TL, Jang TN, Huang CH, et al. Establishing a clinical decision rule of severe acute respiratory syndrome at the emergency department. Ann Emerg Med 2004;43:17–22.

    Article  PubMed  CAS  Google Scholar 

  20. Gibbs P, Turnbull LW. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 2003;50:92–98.

    Article  PubMed  Google Scholar 

  21. Joo S, Yang YS, Moon WK, Kim HC. Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features. IEEE Transact Med Imaging 2004;23:1292–1300.

    Article  Google Scholar 

  22. Walsh P, Cunningham P, Rothenberg SJ, O’Doherty S, Hoey H, Healy R. An artificial neural network ensemble to predict disposition and length of stay in children presenting with bronchiolitis. Eur J Emerg Med 2004;11:259–564.

    Article  PubMed  Google Scholar 

  23. Burroni M, Corona R, Dell’Eva G, et al. Melanoma computer-aided diagnosis: reliability and feasibility study. Clin Cancer Res 2004;10:1881–1886.

    Article  PubMed  Google Scholar 

  24. Press WH, Flannery BP, Teukolsky SA, Vetterling WT. Numerical recipes in FORTRAN example book: the art of scientific computing. 2nd Ed New York: Cambridge University Press; 1992.

    Google Scholar 

  25. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000;406:747–752.

    Article  PubMed  CAS  Google Scholar 

  26. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001;98:10869–10874.

    Article  PubMed  CAS  Google Scholar 

  27. Zellner BB, Rand SD, Prost R, Krouwer H, Chetty VK. A cost-minimizing diagnostic methodology for discrimination between neoplastic and non-neoplastic brain lesions: utilizing a genetic algorithm. Acad Radiol 2004;11:169–177.

    Article  PubMed  Google Scholar 

  28. Bozcuk H, Bilge U, Koyuncu E, Gulkesen H. An application of a genetic algorithm in conjunction with other data mining methods for estimating outcome after hospitalization in cancer patients. Med Sci Monit 2004;10: CR246–CR251.

    PubMed  Google Scholar 

  29. Benenson Y, Gil B, Ben-Dor U, Adar R, Shapiro E. An autonomous molecular computer for logical control of gene expression [see comment]. Nature 2004; 429:423–429.

    Article  PubMed  CAS  Google Scholar 

  30. Huber S, Medl M, Vesely M, Czembirek H, Zuna I, Delorme S. Ultrasonographic tissue characterization in monitoring tumor response to neoadjuvant chemotherapy in locally advanced breast cancer (work in progress). J Ultrasound Med 2000;19:677–686.

    PubMed  CAS  Google Scholar 

  31. Christodoulou CI, Pattichis CS. Unsupervided pattern recognition for the classification of EMG signals. IEEE Trans Biomed Eng 1999;46:169–178.

    Article  PubMed  CAS  Google Scholar 

  32. Banez LL, Prasanna P, Sun L, et al. Diagnostic potential of serum proteomic patterns in prostate cancer. J Urol 2003;170(2 Pt 1):442–426.

    Article  PubMed  CAS  Google Scholar 

  33. Leonard JE, Colombe JB, Levy JL. Finding relevant references to genes and proteins in Medline using a Bayesian approach. Bioinformatics 2002;18:1515–1522.

    Article  PubMed  CAS  Google Scholar 

  34. Bins M, van Montfort LH, Timmers T, Landeweerd GH, Gelsema ES, Halie MR. Classification of immature and mature cells of the neutrophil series using morphometrical parameters. Cytometry 1983;3:435–438.

    Article  PubMed  CAS  Google Scholar 

  35. Hibbard LS, McKeel DW Jr. Automated identification and quantitative morphometry of the senile plaques of Alzheimer’s disease. Anal Quant Cytol Histol 1997;19:123–138.

    PubMed  CAS  Google Scholar 

  36. Baumgartner C, Bohm C, Baumgartner D, et al. Supervised machine learning techniques for the classification of metabolic disorders in newborns. Bioinformatics 2004;20:2985–2996.

    Article  PubMed  CAS  Google Scholar 

  37. Gordon HS, Johnson ML, Wray NP, et al. Mortality after noncardiac surgery: prediction from administrative versus clinical data. Med Care 2005;43:159–167.

    Article  PubMed  Google Scholar 

  38. Bertani A, Cappello A, Benedetti MG, Simoncini L, Catani F. Flat foot functional evaluation using pattern recognition of ground reaction data. Clin Biomech 1999;14:484–493.

    Article  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Culverhouse College of Commerce, University of Alabama, Tuscaloosa, AL, 36497, USA

    J. Michael Hardin PhD (Professor of Statistics and Associate Dean for Research)

  2. Department of Pathology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

    David C. Chhieng MD, MBA, MSHI (Associate Professor)

Authors
  1. J. Michael Hardin PhD
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  2. David C. Chhieng MD, MBA, MSHI
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Editor information

Editors and Affiliations

  1. Health Informatics Program Department of Health Services Administration School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

    Eta S. Berner EdD, FACMI, FHIMSS (Professor) (Professor)

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© 2007 Springer Science+Business Media, LLC

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Hardin, J.M., Chhieng, D.C. (2007). Data Mining and Clinical Decision Support Systems. In: Berner, E.S. (eds) Clinical Decision Support Systems. Health Informatics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-38319-4_3

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  • DOI: https://doi.org/10.1007/978-0-387-38319-4_3

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-33914-6

  • Online ISBN: 978-0-387-38319-4

  • eBook Packages: MedicineMedicine (R0)

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