Expert systems: perils and promise
Communications of the ACM
ACM Computing Surveys (CSUR)
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Hierarchical Decision Tree Induction in Distributed Genomic Databases
IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
International Journal of Hybrid Intelligent Systems
Learning on the border: active learning in imbalanced data classification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
HICSS '11 Proceedings of the 2011 44th Hawaii International Conference on System Sciences
A privacy-preserving framework for distributed clinical decision support
ICCABS '11 Proceedings of the 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences
ICMLA '12 Proceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 02
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When a medical practitioner encounters a patient with rare symptoms that translates to rare occurrences in the local database, it is quite valuable to draw conclusions collectively from such occurrences in other hospitals. However, for such rare conditions, there will be a huge imbalance in classes among the relevant base population. Due to regulations and privacy concerns, collecting data from other hospitals will be problematic. Consequently, distributed decision support systems that can use just the statistics of data from multiple hospitals are valuable. We present a system that can collectively build a distributed classification model dynamically without the need of patient data from each site in the case of imbalanced data. The system uses a voting ensemble of experts for the decision model. The imbalance condition and number of experts can be determined by the system. Since only statistics of the data and no raw data are required by the system, patient privacy issues are addressed. We demonstrate the outlined principles using the Nationwide Inpatient Sample (NIS) database. Results of experiments conducted on 7,810,762 patients from 1050 hospitals show improvement of 13.68% to 24.46% in balanced prediction accuracy using our model over the baseline model, illustrating the effectiveness of the proposed methodology.