The Strength of Weak Learnability
Machine Learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Computers and Biomedical Research
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
On the Accuracy of Meta-learning for Scalable Data Mining
Journal of Intelligent Information Systems
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Improving electronic health records retrieval using contexts
Expert Systems with Applications: An International Journal
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Objective: Clinicians face numerous information needs during patient care activities and most of these needs are not met. Infobuttons are information retrieval tools that help clinicians to fulfill their information needs by providing links to on-line health information resources from within an electronic medical record (EMR) system. The aim of this study was to produce classification models based on medication infobutton usage data to predict the medication-related content topics (e.g., dose, adverse effects, drug interactions, patient education) that a clinician is most likely to choose while entering medication orders in a particular clinical context. Design: We prepared a dataset with 3078 infobutton sessions and 26 attributes describing characteristics of the user, the medication, and the patient. In these sessions, users selected one out of eight content topics. Automatic attribute selection methods were then applied to the dataset to eliminate redundant and useless attributes. The reduced dataset was used to produce nine classification models from a set of state-of-the-art machine learning algorithms. Finally, the performance of the models was measured and compared. Measurements: Area under the ROC curve (AUC) and agreement (kappa) between the content topics predicted by the models and those chosen by clinicians in each infobutton session. Results: The performance of the models ranged from 0.49 to 0.56 (kappa). The AUC of the best model ranged from 0.73 to 0.99. The best performance was achieved when predicting choice of the adult dose, pediatric dose, patient education, and pregnancy category content topics. Conclusion: The results suggest that classification models based on infobutton usage data are a promising method for the prediction of content topics that a clinician would choose to answer patient care questions while using an EMR system.