Abstract
Most statistical, Machine Learning and Data Mining algorithms assume that the data they use is a random sample drawn from a stationary distribution. Unfortunately, many of the databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them may have changed during this time, sometimes radically (this is also known as a concept drift). In clinical institutions, where the patients’ data are regularly stored in a central computer databases, similar situations may occur. Expert physicians may easily, even unconsciously, adapt to the changed environment, whereas Machine Learning and Data Mining tools may fail due to their underlaying assumptions. It is therefore important to detect and adapt to the changed situation. In the paper we review several techniques for dealing with concept drift in Machine Learning and Data Mining frameworks and evaluate their use in clinical studies with a case study of coronary artery disease diagnostics.
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Kukar, M. (2003). Drifting Concepts as Hidden Factors in Clinical Studies. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds) Artificial Intelligence in Medicine. AIME 2003. Lecture Notes in Computer Science(), vol 2780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39907-0_49
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DOI: https://doi.org/10.1007/978-3-540-39907-0_49
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