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Obstacles and Misunderstandings Facing Medical Data Mining

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4093))

Abstract

Medical Data Mining is a very active and challenging research area in Data Mining community. However researchers entering Medical Data Mining should be aware that in core clinical, dentistry and nursing, data mining is not welcomed as much as we believe and publication of results in these journals based on Data Mining algorithms is not easily possible. In this paper, in addition to presenting one of our “successful” KDD projects in Urology that did not get to anywhere, we back up our belief based on designed searches on PubMed and review literature based on these searches. Our findings suggest that few Data Mining algorithms made their ways into core clinical journals. The paper concludes by reasons we have collected through our experiences.

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References

  1. Inokuchi, A., Washio, T., Motoda, H.: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Zighed, A.D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  2. Inokuchi, A., Washio, T., Okada, T., Motoda, H.: Applying the a priori-based graph mining method to mutagenesis data analysis. J. Comput. Aided Chem. 2, 87–92 (2001)

    Article  Google Scholar 

  3. Cios, K.J.: Uniqueness of medical data mining. Artificial Intelligence in Medicine 26(1-2), 1–24 (2002)

    Article  Google Scholar 

  4. McAullay, D., Williams, G., Chen, J., Jin, H., He, H., Sparks, R., Kelman, C.: A Delivery Framework for Health Data Mining and Analytics. In: The 28th Australasian Computer Science Conference. Conferences in Research and Practice in Information Technology, The University of Newcastle, Australia, vol. 38 (2005)

    Google Scholar 

  5. Lavrac, N.: Selected techniques for data mining in medicine. Artificial Intelligence in Medicine 16, 3–23 (1999)

    Article  Google Scholar 

  6. Sakamoto, N.: Object-oriented development of a concept learning system for time-centered clinical data. J Med Syst. 20(4), 183–196 (1996)

    Article  Google Scholar 

  7. Lucas, P.: Bayesian analysis, pattern analysis, and data mining in health care. Curr. Opin. Crit. Care 10(5), 399–403 (2004)

    Article  Google Scholar 

  8. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed (last visited April 15, 2006)

  9. Zaiane, O.: Personal communications of first author through email during February and March (2006)

    Google Scholar 

  10. Ohrn, A., Rowland, T.: Rough sets: a knowledge discovery technique for multifactorial medical outcomes. Am. J. Phys. Med. Rehabil. 79(1), 100–108 (2000)

    Article  Google Scholar 

  11. Aoki, N., Wall, M.J., Demsar, J., et al.: Predictive model for survival at the conclusion of a damage control laparotomy. Am. J. Surg. 180(6), 540-4, discussion 544-5 (December 2000)

    Google Scholar 

  12. Strum, D.P., Sampson, A.R., May, J.H., Vargas, L.G.: Surgeon and type of anesthesia predict variability in surgical procedure times. Anesthesiology 92(5), 1454–1466 (2000)

    Article  Google Scholar 

  13. Elevitch, F.R., Silvers, A., Sahl, J.D.: Projecting corporate health plan utilization and charges from annual ICD-9-CM diagnostic rates: a value-added opportunity for pathologists. Arch. Pathol. Lab. Med. 121(11), 1187–1191 (1997)

    Google Scholar 

  14. Rider, L.G., Giannini, E.H., et al.: International consensus on preliminary definitions of improvement in adult and juvenile myositis. Arthritis Rheum. 50(7), 2281–2290 (2004)

    Article  Google Scholar 

  15. Zeggini, E., Thomson, W., Kwiatkowski, D., Richardson, A., Ollier, W., Donn, R.: Linkage and association studies of single-nucleotide polymorphism-tagged tumor necrosis factor haplotypes in juvenile oligoarthritis. Arthritis Rheum. 46(12), 3304–3311 (2002)

    Article  Google Scholar 

  16. Coulter, D.M., Bate, A., Meyboom, R.H., Lindquist, M., Edwards, I.R.: Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study. BMJ 322(7296), 1207–1209 (2001)

    Article  Google Scholar 

  17. Zoutman, D.E., Ford, B.D., Bassili, A.R.: A call for the regulation of prescription data mining. CMAJ 163(9), 1146–1148 (2000)

    Google Scholar 

  18. Langmann, T., Moehle, C., Mauerer, R., Scharl, M., Liebisch, G., Zahn, A., Stremmel, W., Schmitz, G.: Loss of detoxification in inflammatory bowel disease: dysregulation of pregnane X receptor target genes. Gastroenterology 127(1), 26–40 (2004)

    Article  Google Scholar 

  19. Viguerie, N., Clement, K., et al.: In vivo epinephrine-mediated regulation of gene expression in human skeletal muscle. J Clin Endocrinol Metab. 89(5), 2000–2014 (2004)

    Article  Google Scholar 

  20. Mundt, J.C., Freed, D.M., Greist, J.H.: Lay person-based screening for early detection of Alzheimer’s disease: development and validation of an instrument. J. Gerontol B Psychol. Sci. Soc. Sci 55(3), P163–170 (2000)

    Google Scholar 

  21. Sanz, E.J., De-las-Cuevas, C., Kiuru, A., Bate, A., Edwards, R.: Selective serotonin reuptake inhibitors in pregnant women and neonatal withdrawal syndrome: a database analysis. Lancet 365(9458), 482–487 (2005)

    Article  Google Scholar 

  22. Papadopoulos, M.C., Abel, P.M., Agranoff, D., et al.: A novel and accurate diagnostic test for human African trypanosomiasis. Lancet 363(9418), 1358–1363 (2004)

    Article  Google Scholar 

  23. Ostermeier, G.C., Dix, D.J., Miller, D., Khatri, P., Krawetz, S.A.: Spermatozoal RNA profiles of normal fertile men. Lancet 360(9335), 772–777 (2002)

    Article  Google Scholar 

  24. Goodwin, L.K., Iannacchione, M.A., Hammond, W.E., Crockett, P., Maher, S., Schlitz, K.: Data mining methods find demographic predictors of preterm birth. Nurs. Res. 50(6), 340–345 (2001)

    Article  Google Scholar 

  25. Bate, A., Lindquist, M., Edwards, I.R., Olsson, S., Orre, R., Lansner, A., et al.: A Bayesian neural network method for adverse drug reaction signal generation. Eur. J. Clin Pharmacol. 54, 315–321 (1998)

    Article  Google Scholar 

  26. Harris Jr., J.M.: Coronary angiography and its complications. The search for risk factors. Arch. Intern. Med. 144(2), 337–341 (1984)

    Article  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Sami, A. (2006). Obstacles and Misunderstandings Facing Medical Data Mining. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_93

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  • DOI: https://doi.org/10.1007/11811305_93

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37025-3

  • Online ISBN: 978-3-540-37026-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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