Advanced decision support for complex clinical decisions
- 1
- 2
- 3
Abstract
A Physician’s decision-making skills are directly related to the patient’s positive outcomes. Therefore, a wealth of medical knowledge and clinical experience are key assets for a physician to have. The goal here is to use historical clinical data and relationships processed by Artificial Intelligence (AI) techniques to aid physicians in their decision making process. Presenting this information in a Clinical Decision Support System (CDSS) is an effective means to consolidate decision results. The CDSS provides a large number of medical support functions to help clinicians make the most reasonable diagnosis and choose the best treatment measures. Initial results have shown great promise in accurately predicting Fibrosis Stage in Hepatitis patients. Utilizing this tool could mitigate the need for some liver biopsies in the more than 170 million Hepatitis patients worldwide. The prototype is extendable to accommodate additional techniques (for example genetic algorithms and logistics regression) and additional medical domain solutions (for example HIV/AIDS).
- Begg, R. (2009) Artificial intelligence techniques in medicine and health care. Concepts, Methodologies, Tools, and Applications, Sugumaran, V., Ed., 48(12), 1-99.
- Perreault, L.E. and Metzger, J.B. (1999) A pragmatic framework for understanding clinical decision support. In: Middleton, B., Ed., Clinical decision support systems. Journal of Healthcare Information Management, 13(2), 5-21.
- Enrico, C. (2003) Guide to health informatics. 2nd Edition, Chapter 19, Artificial Intelligence in Medicine: An Introduction. Hodder Arnold, Arnold.
- Robert, S.L. and Lee, B.L. (1959) Reasoning foundations of medical diagnosis: Symbolic logic, probability, and value theory aid our understanding of how physicians reason. American Association for the Advancement of Science, 130(3366), 9-21.
- Špečkauskienė, V. and Lukoševičius, A. (2009) Methodology of adaptation of data mining methods for medical decision support: Case study. Data Mining, 9(2), 228- 235.
- Han, J. and Kamber, M. (2001) Data mining concepts and techniques. 2nd Edition, Academic, Kluwer.
- Taylor, J.G. (1996) Neural networks and their applications. John Wiley and Sons, New York.
- http://janis7hepc.com/biopsies1.htm
- http://www.cs.waikato.ac.nz/ml/weka/
- http://www.keltch.com/neuro3.html
- http://lisp.vse.cz/challenge/ecmlpkdd2005/