Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Proceedings of the 17th ACM conference on Information and knowledge management
Sentiment analysis of blogs by combining lexical knowledge with text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Modeling annotation time to reduce workload in comparative effectiveness reviews
Proceedings of the 1st ACM International Health Informatics Symposium
A unified approach to active dual supervision for labeling features and examples
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Inactive learning?: difficulties employing active learning in practice
ACM SIGKDD Explorations Newsletter
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Medical researchers looking for evidence pertinent to a specific clinical question must navigate an increasingly voluminous corpus of published literature. This data deluge has motivated the development of machine learning and data mining technologies to facilitate efficient biomedical research. Despite the obvious labor-saving potential of these technologies and the concomitant academic interest therein, however, adoption of machine learning techniques by medical researchers has been relatively sluggish. One explanation for this is that while many machine learning methods have been proposed and retrospectively evaluated, they are rarely (if ever) actually made accessible to the practitioners whom they would benefit. In this work, we describe the ongoing development of an end-to-end interactive machine learning system at the Tufts Evidence-based Practice Center. More specifically, we have developed abstrackr, an online tool for the task of citation screening for systematic reviews. This tool provides an interface to our machine learning methods. The main aim of this work is to provide a case study in deploying cutting-edge machine learning methods that will actually be used by experts in a clinical research setting.