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    Patient Safety As An Interactional Achievement: Conversational Analysis In The Trauma Center Of An Inner City Hospital

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    In this dissertation, I apply the methodology of Conversational Analysis to highlight the informal communication of an emergency room work group with the objective of discovering recurrent patterns of interaction and the inherent relational work necessary to accomplish the safe medical care of patients in a Trauma Code on a level of safety comparative to that of ultra-safe systems as described in the literature of High Reliability Organizations. The significance of relational elements of interaction on emerging social order is highlighted in processes of attunement, or the diminishing of difference of status in the use of mitigated speech and the co-construction of narrative. The use of mitigated speech and narrative serve as conversational moves of consequence, by which participants seek cooperation, coordination, and collaborate in face-to-face interaction, in a mutually constructed course of action; that is, in providing safe medical care in a highly complex and high risk environment

    Transfer Learning across Networks for Collective Classification

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    This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201
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