By Andrey Rzhetsky (auth.), Mor Peleg, Nada Lavrač, Carlo Combi (eds.)
This publication constitutes the refereed lawsuits 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; information mining; detailed consultation on AI purposes; probabilistic modeling and reasoning; terminologies and ontologies; temporal reasoning and temporal info mining; treatment making plans, scheduling and guideline-based care; and ordinary language processing.
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Extra resources for Artificial Intelligence in Medicine: 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011. Proceedings
While, instrumental provision (IsF) is calculated using the combination of agreeableness (PaG), perceived close tie (PcT), and experience in supportive exchange (EsE). Emotional support provision (EsF) depends on perceived close tie, and agreeableness. Finally, companionship support provision (CsF) requires provider mutual interest, perceived close tie, and extraversion personality (PeV). WsHb(t) (11) (12) (13) (14) Provided Support: In general, specific supports (informational (IfP), emotional (EsP), instrumental (InP), and (CsP)) can be measured by combining some proportion of proactive effort (PaC), and an active observation of long term stress (AoS) with particular support preference attributes and support requests (informational (RfR),direct emotional (DeR), indirect emotional (PiE), instrumental (RnR), and companionship (HcR ) support requests).
1 Support Assignment In this case study, eleven different fictional persons are studied under several parameters and attributes for social support receipt and provision. Consider this example: “Piet experiences stress and seeks for help. 3). 2). 1). In this case, three individuals were assigned to provide help. Note that this information is generated from the dynamic model of support receipt and provision process. Using a support tie preference, he prefers 78 % from support members in a weak tie (≈ 2 members), and 22 % from a strong tie (≈ 1 member).
2. No counter examples were found by the method and thus the ﬁrst iteration was concluded. New rules were induced before entering the next iteration. Bradykinesia = false THEN class = EMT; [20,0] The rule covers 20 learning examples, and all of them are from class ET. 2 was classiﬁed as purely ET by the neurologist. 61 was classiﬁed as both PT and ET. In such a case, the expert is asked to describe which features are in favor of ET and which features are in favor of PT. The expert explained that the presence of postural tremor speaks in favor of ET, while the presence of rigidity speaks in favor of PT.
Artificial Intelligence in Medicine: 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011. Proceedings by Andrey Rzhetsky (auth.), Mor Peleg, Nada Lavrač, Carlo Combi (eds.)