Abstract
The paper presents an interactive discovery support system for the field of medicine. The intended users of the system are medical researchers. The goal of the system is: for a given starting concept of interest, discover new, potentially meaningful relations with other concepts that have not been published in the medical literature before. We performed two types of preliminary evaluation of the system: 1) by a medical doctor and 2) by automatic means. The preliminary evaluation showed that our approach for supporting discovery in medicine is promising, but also that some further work is needed, especially on limiting the number of potential discoveries the system generates.
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Swanson, D.R.: Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspect Biol Med. 1986 Autumn;30(1):7–18.
Swanson, D.R.: Online search for logically-related noninteractive medical literatures: a systematic trial-and-error strategy. J Am Soc Inf Sci. 1989 Sep;40(5):356–8.
Swanson, D.R., Smalheiser, N.R.: An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artif. Intell. 91 (1997) 183–203.
U.S. National Library of Medicine. http://www.nlm.nih.gov/<30.04.2000>
Humphreys, B.L., Lindberg, D.A.B., Schoolman, H.M., Barnett, G.O.: The Unified Medical Language System: an informatics research collaboration. JAMIA 1998;5(1):1–11.
Agrawal, R. et al: Fast discovery of association rules. In U. Fayyad et al, editors, Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge, MA. (1996)
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Hristovski, D., Džeroski, S., Peterlin, B., Rožić, A. (2000). Supporting Discovery in Medicine by Association Rule Mining of Bibliographic Databases. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_49
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DOI: https://doi.org/10.1007/3-540-45372-5_49
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