Scouting for imprecise temporal associations to support effectiveness of drugs during clinical trials

• Published in: NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society pp. 171 - 176
• Main Authors: Vincenti, G., Hammell, R.J., Trajkovski, G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: a-priori algorithm; Algorithm design and analysis; Clinical trials; Clustering algorithms; Data analysis; Data mining; Drugs; Fuzzy sets; market-basket algorithm; medical informatics; Pattern analysis; Pediatrics; temporal associations; Testing
• ISBN: 9780780391871; 078039187X
• DOI: 10.1109/NAFIPS.2005.1548528

The field of data mining is dedicated to the analysis of data to find underlying connections and the discovery of new patterns. This research targets the analysis of imprecise temporal associations through the modification of a standard market basket analysis approach by means of fuzzy set relations to classify the associations among different sources of data. The domain that is taken into consideration in this work is the one of medicine. We used data recorded within an Intensive Care Unit from a 8 month old infant that suffers from Acute Respiratory Distress Syndrome. In particular, we analyzed the response of the partial pressure of oxygen within the bloodstream to the application of a respirator. The results of this research show that it is possible to investigate such relations with the help of fuzzy set classification for temporal associations, and the result of such exploration is as easily understandable as the standard Market Basket algorithm. The findings support the physiological response, suggesting that this approach is worthy of notice. We are confident that such an algorithm will show its capabilities when applied to the clinical trials part of drug testing, given the results outlined in this article.