International Journal of Medical Laboratory Research (IJMLR)

APPLICATION OF DATA MINING AND KNOWLEDGE MANAGEMENT IN SPECIAL REFERENCE TO MEDICAL INFORMATICS: A REVIEW

 

Yogita Gupta, Rana Khudhair Abbas Ahmed, Dr. Sandeep Kumar Kautish

​

ABSTRACT:In recent times, while India is moving towards Digital Revolution, Data Mining and Knowledge Management are two of the areas which attract many researchers as both have high potential in terms of developing new techniques to be applied in different domains of human life. Medical science is one of the areas where new inventions and developments are coming up as the results of integration of Data Mining and Knowledge Management tools. The aim of this paper is to present detailed survey of available literatureon recent advancements and notable contributions in the field of applications of Data Mining and Knowledge Management tools especially focused on Medical Informatics. After presenting introduction about the aim and scope of the topic, section two of the paperreestablish the concepts of Data mining and its conventional techniques i.e. Probabilistic & Statistical Models, Rule Induction, Neural Networks and Analytical Learning and the section ends with presenting Knowledge Management concept and its linkage with Data mining and Medical Science field.In section three, all the previous relevant works of Data mining and knowledge management are critically analyzed, explained and categorized on the basis of their applicability which is followed by section four which presentsdiscussion on all theprevious works andhighlight the advantages and disadvantages of various methods and toolswith further scope of future research and limitations. The author concludes the paper while emphasizing on security andprivacy concerns of medical data and attract readers’ attention towards validation of medical data which is used for medical judgments and decisions.

 

KEYWORDS:Knowledge Management, Data Mining, Medical Informatics, Informatics, Medical Decisions

​

REFERENCES:

 

1. Abidi, S. S. R. (2001). "Knowledge Management in Healthcare: Towards 'Knowledgedriven' Decision-support Services," International Journal of Medical Informatics, 63, 5-18.

2. Antonie M. L., Zaiane O. R., and Coman A. (2001), “Application of data mining techniques for medical image classifica-tion,” in Proceedings Second International Workshop on Multimedia Data Mining, pp. 94–101.

3. shwin Belle, Raghuram Thiagarajan, S. M. Reza Soroushmehr, Fatemeh Navidi, Daniel A. Beard, and Kayvan Najarian (2015), “Big Data Analytics in Healthcare,” BioMed Research International, vol. 2015, Article ID 370194, 16 pages, 2015. doi:10.1155/2015/370194

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

5. Carbonell, J. G. Michalski, R. S., Mitchell, T. M. (1983). "An Overview of MachineLearning," in R. S. Michalski, J. G.

6. Clifton, L.; Clifton, D.A.; Pimentel, M.A.F.; Watkinson, P.J.; Tarassenko, L. (2013) Gaussian processes for personalized e-health monitoring with wearable sensors. IEEE Trans. Biomed. Eng., 60, 193–197.

7. C. M. C. Tempany, J. Jayender, T. Kapur et al. (2015), “Multimodal imaging for improved diagnosis and treatment of cancers,”Cancer, vol. 121, no. 6, pp. 817–827

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

9. Delen D., Walker G., and Kadam A. (2005), “Predicting breast cancer survivability: A comparison of three data mining methods,” Artificial Intelligence in Medicine, 34(2), 113–27.

10. Duda, R. 0.and Hart, P. E. (1973). Pattern ClassiJication and Scene Analysis, New York: John Wiley and Sons.

11. F. E. Dewey, M. E. Grove, C. Pan et al. (2014), “Clinical interpretation and implications of whole-genome sequencing,” JAMA, vol. 311, no. 10, pp. 1035–1045

12. Fisher, D. H. (1987)."Knowledge Acquisition via Incremental Conceptual Clustering," Machine Learning, 2, 139-172.

13. Gaura, E.; Kemp, J.; Brusey, J. (2013), Leveraging knowledge from physiological data: On-body heat stress risk prediction with sensor networks. IEEE Trans. Biomed.Circuits System.

14. Huang, G.; Zhang, Y.; Cao, J.; Steyn, M.; Taraporewalla, K. (2013), Online mining abnormal period patterns from multiple medical sensor data streams. World Wide Web, doi:10.1007/s11280-013-0203-y.

15. Hubert, R. (2006). Accessibility and usability guidelines for mobile devices in home healthmonitoring. (84), 26-29.

16. J. M. Blum, H. Joo, H. Lee, and M. Saeed (2015), “Design and implementation of a hospital wide waveform capture system,” Journal of Clinical Monitoring and Computing, vol. 29, no. 3, pp.359–362

17. K. Bernatowicz, P. Keall, P.Mishra, A. Knopf, A. Lomax, and J. Kipritidis (2015), “Quantifying the impact of respiratory-gated 4D CT acquisition on thoracic image quality: a digital phantom study,” Medical Physics, vol. 42, no. 1, pp. 324–334

18. Kohonen, T. (1995).Self-organizing Maps, Springer-Verlag, Berlin.Kononenko, I. (1993). "Inductive and Bayesian Learning in Medical Diagnosis," AppliedArtificial Intelligence, 7,3 17-337

19. Langley, P. and Simon, H. (1995). "Applications of Machine Learning and Rule Induction," Communications of the ACM, 38(1 I), 55-64.

20. Li L., Tang H., Wu Z., Gong J., Gruidl M., Zou J., Tockman M., and Clark R. (2004), “Data mining techniques for cancer detection using serum proteomic profiling,” Artificial Intelligence in Medicine, 32(2), 71–83.

21. L. Qu, F. Long, and H. Peng (2015), “3D registration of biological images and models: registration of microscopic images and its uses in segmentation and annotation,” IEEE Signal Processing Magazine, vol. 32, no. 1, pp. 70–77

22. M. Attin, G. Feld, H. Lemus et al. (2015), “Electrocardiogram characteristics prior to in-hospital cardiac arrest,” Journal of Clinical Monitoring and Computing, vol. 29, no. 3, pp. 385–392

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

24. Prather, J. C., Lobach, D. F., Goodwin, L. K., Hales, J. W., Hage, M. L., and Hammond, W. E. (1997)."Medical Data Mining: Knowledge Discovery in a Clinical Data Warehouse,"cin Proceedings of the AMIA Annual Symposium Fall 1997, 101-105.

25. Philips-Wren G., Sharkey. P., and Morss. S. (2008), “Mining lung cancer patient data to assess healthcare resource utiliza-tion,” Expert Systems with Applications: An International Journal, 35(4), 1611–1619.

26. P. Zikopoulos, C. Eaton, D. deRoos, T. Deutsch, and G. Lapis (2011), Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw-Hill Osborne Media

27. Rumelhart, D. E., Hinton, G. E., and McClelland, J. L. (1986a). "A General Framework for Parallel Distributed Processing," in D. E. Rumelhart, J. L. McClelland, and the PDP Research Group (Eds.), Parallel Distributed Processing, pp. 45-76, Cambridge, MA: The MIT Press.

28. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986b). "Learning Internal

29. Representations by Error Propagation," in D. E. Rumelhart, J. L. McClelland, and the PDP Research Group (Eds.), Parallel Distributed Processing, pp. 318-362, Cambridge, MA: The MIT Press.

30. Su, C. T., Yang, C. H., Hsu, K. H., and Chiu, W. K. (2006), “Data mining for the diagnosis of type II diabetes from three- dimensional body surface anthropometrical scanning data,” Computers & Mathematics with Applications, 51(6–7), 1075–1092.

31. T. Hussain and Q. T. Nguyen (2014), “Molecular imaging for cancer diagnosis and surgery,” Advanced Drug Delivery Reviews, vol. 66, pp. 90–100

32. T. G. Kannampallil, A. Franklin, T. Cohen, and T. G. Buchman (2014), “Sub-optimal patterns of information use: a rational analysisof information seeking behavior in critical care,” in Cognitive Informatics in Health and Biomedicine, pp. 389–408, Springer, London, UK

33. W. Y. Hsu (2015), “Segmentation-based compression: new frontiers of telemedicine in telecommunication,”Telematics and Informatics, vol. 32, no. 3, pp. 475–485

 

​