Knowledge Management Techniques for Analysis of Clinical Databases
In recent years, clinical decision support systems (CDSSs) play a vital role in the field of medical informatics. CDSSs help the medical practitioners in facing challenging medical problems, such as diagnosis and therapy. One of the major difficulties for any medical practitioner is to appropriately and more accurately diagnose a disease. To meet the challenge, an Action Rule based Diagnostic System (ARDS) is proposed for erythemato-squamous disease. An Adaptive Neuro Fuzzy Inference System (ANFIS) mechanism is employed to manage knowledge acquired and found from the system. Action rule and fuzzy rule based classifier have been developed in accordance with the severity levels of the diseases, the results obtained endorses the main objective of the system which is to develop an authentic and reliable tool to reduce the human errors and improve the quality of medical care provided to the public.
KeywordsAction rule Fuzzy rule Knowledge management
Unable to display preview. Download preview PDF.
- 1.Guvenir, H.A., Demiroz, G., Ilter, N.: Learning Differential Diagnosis of Erythemato-Squamous Diseases using VotingFeature Intervals. In: Artificial Intelligence in Medicine. Elsevier science B.V. (1998)Google Scholar
- 3.Abdul, S., Bhagile, V.D., Manza, R.R., Ramteke, R.J.: Diagnosis and Medical Prescription of heart Disease Using Support Vector Machine and Feedforward Backpropagation Technique. International Journal on Computer Science and Engineering (2010)Google Scholar
- 4.Prather, J.C., Lobach, D.F., Goodwin, L.K., Hales, J.W., Hage, M.L., Hammond, W.E.: Medical Data Mining- Knowledge Discovery in a Clinical Data Warehouse. Duke University Medical Centre, North Carolina, AMICA, Inc. (1997)Google Scholar
- 5.Narasingarao, M.R., Manda, R., Sridhar, G.R., Madhu, K., Rao, A.A.: A Clinical Decision Support System Using Multilayer Perceptron Neural Network to Assess Well Being in Diabetes. Journal of Association of Physicians of India 57, 127–133 (2009)Google Scholar
- 6.Mullins, I.M., Siadaty, M.S., Lyman, J., Scully, K., Garrett, C.T., Miller, W.G., Muller, R., Robson, B., Apte, C., Weiss, S., Rigoustos, I., Platt, D., Cohen, S., Knaus, W.A.: Data Mining and Clinical Data repositories.: Insights from a 667,000 patient dataset. Computers in Biology and Medicine 36, 1351–1377 (2006)CrossRefGoogle Scholar
- 8.Kadhim, Q., Al-Shayea, Bahia, I.S.H.: Urinary System Diseases diagnosis Using Artifical Neural Networks. IJCSNS International Journal of Computer Science and Network Security (2010)Google Scholar
- 9.The Evolving Role of Knowledge Management in Medicine, www.ikmagazine.com/xq/asp/sid.0/articleid.9533CA24-EFBC-4D26-AFB7-2BB8FC2E039A/eTitle.The_Evolving_Role_of_Knowledge_Management_in_Medicine/qx/news.asp
- 10.R Development Core Team.: R: A language and environment for statistical computing. In: R Foundation for Statistical Computing, Vienna, Austria (2005)Google Scholar
- 11.UCI, Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html
- 12.Al-Hawamdeh, S.: Knowledge management: re-thinking information management and facing the challenge of managing tacit knowledge. Information Research 8(1), 143 (2002)Google Scholar
- 13.Holsheimer, M., Siebes, A.: Data Mining: the search for Knowledge in Databases. Technical report CS-R9406, CWI (1994)Google Scholar