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The Distributed ID3-based Decision Tree (DIDT) algorithm provides a basis for Distributed Privacy-preserving Clinical Decision Support Systems. Due to large number of features associated with clinical patient records and iterative nature of distributed algorithms, exchanging information related to all features is expensive. We show that auto-reduction for features can be achieved with significant...
Building prediction models for suggestive knowledge from multiple sources dynamically is of great interest from a clinical decision support point of view. This is valuable in situations where the local clinical data repository does not have sufficient number of records to draw conclusions from. However, due to privacy concerns, hospitals are reluctant to divulge patient records. Consequently, a distributed...
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