By Andrey Rzhetsky (auth.), Mor Peleg, Nada Lavrač, Carlo Combi (eds.)
This ebook constitutes the refereed complaints of the thirteenth convention on synthetic Intelligence in medication, AIME 2011, held in Bled, Slovenia, in July 2011.The forty two revised complete and brief papers awarded including 2 invited talks have been rigorously reviewed and chosen from 113 submissions. The papers are prepared in topical sections on knowledge-based structures; info mining; certain consultation on AI functions; probabilistic modeling and reasoning; terminologies and ontologies; temporal reasoning and temporal information mining; remedy making plans, scheduling and guideline-based care; and average language processing.
Read or Download Artificial Intelligence in Medicine: 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011. Proceedings PDF
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Additional resources for Artificial Intelligence in Medicine: 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011. Proceedings
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Find the most critical example and present it to the expert. If a critical example can not be found, stop the procedure. 3. Expert explains the example; the explanation is encoded in arguments and attached to the learning example. 4. Return to step 1. To ﬁnalize the procedure, we need to answer the following two questions: a) how do we select critical examples? and b) how can we achieve to get all necessary information for the chosen example? Identifying critical examples. A critical example is an example the current hypothesis can not explain well.
The third possibility occurred only once in the ABML knowledge elicitation process presented in this paper. Bradykinesia meaningless. 2. ” Such changes should not by any circumstances be made after examining results on the testing data, and it is particularly important that the expert relies on his common knowledge of the domain when doing this. Bradykinesia = false THEN class = EMT; [16,0] Although the rule has notably worse coverage, the expert found it consistent with his domain knowledge. The expert approved all the rules in the ﬁnal model and thus the iterative process was concluded.