JSEA  Vol.2 No.3 , October 2009
Data Mining in Biomedicine: Current Applications and Further Directions for Research
ABSTRACT
Data mining is the process of finding the patterns, associations or relationships among data using different analytical techniques involving the creation of a model and the concluded result will become useful information or knowledge. The advancement of the new medical deceives and the database management systems create a huge number of data-bases in the biomedicine world. Establishing a methodology for knowledge discovery and management of the large amounts of heterogeneous data has become a major priority of research. This paper introduces some basic data mining techniques, unsupervised learning and supervising learning, and reviews the application of data mining in biomedicine. Applications of the multimedia mining, including text, image, video and web mining are discussed. The key issues faced by the computing professional, medical doctors and clinicians are highlighted. We also state some foreseeable future developments in the field. Although extracting useful information from raw biomedical data is a challenging task, data mining is still a good area of scientific study and remains a promising and rich field for research.

Cite this paper
nullS. TING, C. SHUM, S. KWOK, A. TSANG and W. LEE, "Data Mining in Biomedicine: Current Applications and Further Directions for Research," Journal of Software Engineering and Applications, Vol. 2 No. 3, 2009, pp. 150-159. doi: 10.4236/jsea.2009.23022.
References
[1]   W. Frawley, G. Piatetsky-Shapiro, and C. Matheus, “Knowl-edge discovery in databases: An overview,” AI Magazine, pp. 213–228, 1992.

[2]   K. J. Cios, W. Pedrycz, R. W. Swiniarski, and L. A. Kurgan, “Data mining: A knowledge discovery approach,” Springer, New York, 2007.

[3]   J. T. Tou and R. C. Gonzalez, “Pattern recognition principles,” Addison-Wesley, London, 1974.

[4]   R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern classifica-tion,” Wiley, 2001.

[5]   T. Hastie, R. Tibshirani, and J. Friedman, “The elements of statistical learning: Data mining, inference, and prediction,” Springer, New York, 2001.

[6]   J. W. Lee, J. B. Lee, M. Park, and S. H. Song, “An extensive comparison of recent classification tools applied to microarray data,” Computational Statistics & Data Analysis, Vol. 48, No. 4, pp. 869–885, 2005.

[7]   V. S. Tseng and S. C. Yang, “Mining multi-level association rules from gene expression profiles and gene ontology,” in Proceedings IEEE Workshop Life Science Data Mining (held with IEEE ICDM), UK, November 2004.

[8]   H. Chen, S. S. Fuller, C. Friedman, and W. Hersh, “Medical informatics–knowledge management and data mining in bio-medicine,” Springer, 2005.

[9]   C. D. Krivda, “Data-Mining Dynamine,” Byte, 1995.

[10]   J. C. Prather, D. F. Lobach, L. K. Goodwin, J. W. Hales, M. L. Hage, and W. E. Hammond, “Medical data mining: Knowledge discovery in a clinical data warehouse,” in Proceedings AMIA Annual Fall Symposium, pp. 101– 105, 1997.

[11]   J. L. Breault, C. R. Goodall, and P. J. Fos, “Data mining a dia-betic data warehouse,” Artificial Intelligence in Medicine, Vol. 26, pp. 37–54, 2002.

[12]   A. M. Wilson, L. Thabane, and A. Holbrook, “Application of data mining techniques in pharmacovigilance,” British Journal of Clinical Pharmacology, Vol. 57, No. 2, pp. 127–134, 2004.

[13]   J. Lian, C. Cotrutz, and L. Xing, “Therapeutic treatment plan optimization with probability density-based dose prescription,” Medical Physics, Vol. 30, No. 4, pp. 655– 666, 2003.

[14]   E. G. Susan and J. M. Warren, “Statistical modelling of general practice medicine for computer assisted data entry in electronic medical record systems,” International Journal of Medical In-formatics, Vol. 57, No. 2-3, pp. 77–89, 2000.

[15]   J. R. Warren, A. Davidovic, S. Spenceley, and P. Bolton, “Mediface: Anticipative data entry interface for general practi-tioners,” in Proceedings Computer Human Interaction Confer-ence 1998, pp. 192–199, 1998.

[16]   R. J. Kuo, S. Y. Lin, and C. W. Shih, “Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan,” Expert Systems with Applications, Vol. 33, pp. 794–808, 2007.

[17]   Z. Y. Zhuang, L. Churilov, F. Burstein, and K. Sikaris, “Com-bining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitio-ners,” European Journal of Operational Research, Vol. 195, No. 3, pp. 662–675, 2009.

[18]   M. J. Huang, M. Y. Chen, and S. C. Lee, “Integrating data mining with case-based reasoning for chronic diseases progno-sis and diagnosis,” Expert Systems with Applications, Vol. 32, No. 3, pp. 856–867, 2007.

[19]   M. G. Tsipouras, T. P. Exarchos, D. I. Fotiadis, A. P. Kotsia, K. V. Vakalis, K. K. Naka, and L. K. Michalis, “Automated diag-nosis of coronary artery disease based on data mining and fuzzy modeling,” IEEE Transactions on Information Technology in Biomedicine, Vol. 12, No. 4, pp. 447–457, 2008.

[20]   V. Megalooikonomou, J. Ford, L. Shen, F. Makedon, and A. Saykin, “Data mining in brain imaging,” Statistical Methods in Medical Research, Vol. 9, No. 4, pp. 359–394, 2000.

[21]   S. E. Brossette, A. P. Sprague, W. T. Jones, and S. A. Moser, “A data mining system for infection control surveillance,” Methods of Information in Medicine, Vol. 39, No. 4-5, pp. 303–310, 2000.

[22]   M. L. Antonie, O. R. Zaiane, and A. Coman, “Application of data mining techniques for medical image classification,” in Proceedings Second International Workshop on Multimedia Data Mining, pp. 94–101, 2001.

[23]   D. M. Coulter, A. Bate, R. H. B. Meyboom, M. Lindquist, and R. Edwards, “Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: Data mining study,” British Medical Journal, Vol. 322, pp. 1207–1209, 2001.

[24]   L. Li, H. Tang, Z. Wu, J. Gong, M. Gruidl, J. Zou, M. Tockman, and R. Clark, “Data mining techniques for cancer detection using serum proteomic profiling,” Artificial Intelligence in Medicine, Vol. 32, No. 2, pp. 71–83, 2004.

[25]   D. Delen, G. Walker, and A. Kadam, “Predicting breast cancer survivability: A comparison of three data mining methods,” Artificial Intelligence in Medicine, Vol. 34, No. 2, pp. 113–27, 2005.

[26]   C. T. Su, C. H. Yang, K. H. Hsu, and W. K. Chiu, “Data min-ing for the diagnosis of type II diabetes from three- dimensional body surface anthropometrical scanning data,” Computers & Mathematics with Applications, Vol. 51, No. 6–7, pp. 1075–1092, 2006.

[27]   G. Philips-Wren, P. Sharkey, and S. Morss, “Mining lung can-cer patient data to assess healthcare resource utilization,” Ex-pert Systems with Applications: An International Journal, Vol. 35, No. 4, pp. 1611–1619, 2008.

[28]   M. Hearst, “Untangling text data mining,” in the Proceedings ACL’99: The 37th annual meeting of the association for com-putational linguistics, University of Maryland, June 1999.

[29]   H. Chen, “Knowledge management systems: A text mining perspective,” Tucson, AZ, The University of Arizona, 2001.

[30]   K. B. Cohen and L. Hunter, “Getting started in text mining,” PLoS Computational Biology, Vol. 4, No. 1, doi: 10.1371/journal.pcbi.0040020, 2008.

[31]   Y. Ku, C. Chiu, B. H. Liou, J. H. Liou, and J. Y. Wu, “Apply-ing text mining to assist people who inquire HIV/AIDS infor-mation from Internet,” in Proceedings ISI 2008 Workshops, pp. 440–448, 2008.

[32]   D. Zhou, Y. He, and C. K. Kwoh, “Validating text mining re-sults on protein-protein interactions using gene expression pro-files,” in Proceedings International Conference on Biomedical and Pharmaceutical Engineering 2006, pp. 580–585, 2006.

[33]   Y. Peng, B. Yao, and J. Jiang, “Knowledge-discovery incorpo-rated evolutionary search for microcalcification detection in breast cancer diagnosis,” Artificial Intelligence in Medicine, Vol. 37, No. 1, pp. 43–53, 2006.

[34]   H. Pan, Q. Han, X. Xie, Z. Wei, and J. Li, “A Similarity re-trieval method in brain image sequence database,” Advanced Data Mining and Applications, Vol. 4632, pp. 352–364, 2007.

[35]   X. Zhu, W. G. Aref, J. Fan, A.C. Catlin, and A. K. Elmagarmid, “Medical video mining for efficient database indexing, man-agement and access,” in Proceedings 19th International Con-ference on Data Engineering, pp. 569–580, 2003.

[36]   R. Kohavi, B. Masand, M. Spilipoulou, and J. Srivastava, “Web mining,” Data Mining and Knowledge Discovery, Vol. 6, pp. 5–8, 2002.

[37]   W. D. Yu and S. R. Jonnalagadda, “Semantic web and mining in healthcare,” in Proceedings 8th International Conference on e-Health Networking, Applications and Services, pp. 198–201, 2006.

[38]   S. Mitra and T. Acharya, “Data mining: Multimedia, soft com-puting and bioinformatics,” John Wiley & Sons, Inc., New Jersey, 2003.

 
 
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