Medical knowledge discovery systems: data abstraction and performance measurement

Knowledge discovery systems can be traced back to their origin, artificial intelligence and expert systems, but use the modern technique of data mining for the knowledge discovery process. To that end, the technical community views data mining as one step in the knowledge discovery process, while the non-technical community seems to view it as encompassing all of the steps to knowledge discovery. In this exploratory study, we look at medical knowledge discovery systems (MKDSs) by first looking at three examples of expert systems to generate medical knowledge. We then expand on the use of data abstraction as a pre-processing step in the comprehensive task of medical knowledge discovery. Next, we look at how performance of a medical knowledge discovery system is measured. Finally, the conclusions point to a bright future for MKDSs, but an area that needs extensive development to reach its full potential.

[1]  Philip J. Pratt,et al.  Database systems - management and design , 1987 .

[2]  P. Maurette [To err is human: building a safer health system]. , 2002, Annales francaises d'anesthesie et de reanimation.

[3]  Izak Benbasat,et al.  Explanations From Intelligent Systems: Theoretical Foundations and Implications for Practice , 1999, MIS Q..

[4]  Bruce G. Buchanan,et al.  The MYCIN Experiments of the Stanford Heuristic Programming Project , 1985 .

[5]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[6]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[7]  Makoto Haraguchi,et al.  Data abstractions for decision tree induction , 2003, Theor. Comput. Sci..

[8]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[9]  Blaz Zupan,et al.  Intelligent Data Analysis in Medicine , 2000 .

[10]  Karim K. Hirji,et al.  Discovering data mining: from concept to implementation , 1999, SKDD.

[11]  Regina Barzilay,et al.  A New Approach To Expert System Explanations , 1998, INLG.

[12]  Efraim Turban,et al.  Integrating Expert Systems and Decision Support Systems , 1986, MIS Q..

[13]  Silvia Miksch,et al.  Utilizing temporal data abstraction for data validation and therapy planning for artificially ventilated newborn infants , 1996, Artif. Intell. Medicine.

[14]  Ursula Gather,et al.  Pattern Recognition in Intensive Care Online Monitoring , 2001 .

[15]  L. Kohn,et al.  To Err Is Human : Building a Safer Health System , 2007 .

[16]  Vipin Kumar,et al.  Emerging scientific applications in data mining , 2002, CACM.

[17]  Yuval Shahar,et al.  Knowledge-based temporal abstraction in clinical domains , 1996, Artif. Intell. Medicine.

[18]  S Miksch,et al.  Effective data validation of high-frequency data: Time-point-, time-interval-, and trend-based methods , 1997, Comput. Biol. Medicine.

[19]  Katharina Morik,et al.  Knowledge discovery and knowledge validation in intensive care , 2000, Artif. Intell. Medicine.

[20]  Ephraim R. McLean,et al.  Information Systems Success: The Quest for the Independent Variables , 1992, J. Manag. Inf. Syst..

[21]  Nada Lavrac,et al.  Selected techniques for data mining in medicine , 1999, Artif. Intell. Medicine.

[22]  Peter Clark,et al.  The CN2 Induction Algorithm , 1989, Machine Learning.

[23]  A. Feeldersa,et al.  Methodological and practical aspects of data mining , 2000 .