Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Fast discovery of association rules
Advances in knowledge discovery and data mining
Solving regression problems with rule-based ensemble classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Treatment of Missing Values for Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
CCCS: a top-down associative classifier for imbalanced class distribution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient discovery of risk patterns in medical data
Artificial Intelligence in Medicine
Literature-based discovery: Beyond the ABCs
Journal of the American Society for Information Science and Technology
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Automated procedures are described for discovering predictive rules from electronic health records. These patient records are structured, but are not collected relative to any targeted labels or study objectives. The learning methods cycle through all features, simulating labels and converting the problem from unlabeled learning to supervised classification and regression. Each feature in turn is processed as a simulated label, and a prediction is made from the remaining features. Using a decision-rule representation for knowledge extraction, machine learning techniques are applied to a large collection of electronic health records. Many rules are readily induced with significant predictive performance. By formulating the rules as queries to a web search engine, and then counting hit frequencies, we show how medical researchers can assess and rank potential for new insight among a collection of empirically strong associations.