Data mining has been used very frequently to extract
hidden information from large databases. This paper suggests the use
of decision trees for continuously extracting the clinical reasoning in
the form of medical expert-s actions that is inherent in large number
of EMRs (Electronic Medical records). In this way the extracted data
could be used to teach students of oral medicine a number of orderly
processes for dealing with patients who represent with different
problems within the practice context over time.
[1] A Computerised Teaching Aid in Oral Medicine and Oral Pathology.
Mats Jontell, Oral medicine, Sahlgrenska Academy, Göteborg
University. Olof Torgersson, department of Computing Science,
Chalmers University of Technology, Göteborg.
[2] T. Mitchell, "Decision Tree Learning", in T. Mitchell, Machine
Learning, the McGraw-Hill Companies, Inc., 1997, pp. 52-78.
[3] P. Winston, "Learning by Building Identification Trees", in P. Winston,
Artificial Intelligence, Addison-Wesley Publishing Company, 1992, pp.
423-442.
[4] Howard J. Hamilton-s CS Course: Knowledge Discovery in Databases.
Accessed 06/06/12.
[5] http://www.cs.waikato.ac.nz/ml/weka/, accessed 06/05/21.
[6] http://grb.mnsu.edu/grbts/doc/manual/J48_Decision_T rees.html,
accessed 06/06/12.
[7] Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan
Kauffman, 1993.
[1] A Computerised Teaching Aid in Oral Medicine and Oral Pathology.
Mats Jontell, Oral medicine, Sahlgrenska Academy, Göteborg
University. Olof Torgersson, department of Computing Science,
Chalmers University of Technology, Göteborg.
[2] T. Mitchell, "Decision Tree Learning", in T. Mitchell, Machine
Learning, the McGraw-Hill Companies, Inc., 1997, pp. 52-78.
[3] P. Winston, "Learning by Building Identification Trees", in P. Winston,
Artificial Intelligence, Addison-Wesley Publishing Company, 1992, pp.
423-442.
[4] Howard J. Hamilton-s CS Course: Knowledge Discovery in Databases.
Accessed 06/06/12.
[5] http://www.cs.waikato.ac.nz/ml/weka/, accessed 06/05/21.
[6] http://grb.mnsu.edu/grbts/doc/manual/J48_Decision_T rees.html,
accessed 06/06/12.
[7] Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan
Kauffman, 1993.
@article{"International Journal of Information, Control and Computer Sciences:64029", author = "Fahad Shahbaz Khan and Rao Muhammad Anwer and Olof Torgersson and Göran Falkman", title = "Data Mining in Oral Medicine Using Decision Trees", abstract = "Data mining has been used very frequently to extract
hidden information from large databases. This paper suggests the use
of decision trees for continuously extracting the clinical reasoning in
the form of medical expert-s actions that is inherent in large number
of EMRs (Electronic Medical records). In this way the extracted data
could be used to teach students of oral medicine a number of orderly
processes for dealing with patients who represent with different
problems within the practice context over time.", keywords = "Data mining, Oral Medicine, Decision Trees,WEKA.", volume = "2", number = "1", pages = "220-6", }