Data Mining and Statistics in Medicine:
	     An Application in Prostate Cancer Detection

      Vida S. Tigrani                  George H. John
      UCSF School of Medicine S245     Epiphany
      San Francisco, CA 94143-0454     2300 Geng Rd, Palo Alto, CA 94303
      vtigran@itsa.ucsf.edu            gjohn@epiphany.com


  Data mining is an umbrella term referring to the process of
  discovering patterns in data, typically with the aid of powerful
  algorithms to automate part of the search.  These methods come from
  disciplines such as statistics, machine learning (artificial
  intelligence), pattern recognition, neural networks, and databases.
  Two data analysts with different heritages might approach a similar
  problem quite differently.  In particular, this paper shows how the
  same problem, prostate cancer detection, is approached by an M.D.
  and a data mining analyst with a background in machine learning:
  hypothesis testing by the former, and bagged classification models
  by the latter.  We then survey medical data analysis literature,
  describing the common statistical methods employed by physicians for
  clinical studies, and the advances in data mining and machine
  learning research that have been motivated by medical data analysis.

Citation:

Tigrani, Vida and John, George H. (1998) Data Mining and Statistics
in Medicine: An Application in Prostate Cancer Detection.  In JSM98,
the Proceedings of the Joint Statistical Meetings, Section on 
Physical and Engineering Sciences. American Statistical Association.