Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Journal of Machine Learning Research
A Concept-Driven Algorithm for Clustering Search Results
IEEE Intelligent Systems
Co-clustering by block value decomposition
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Spectral clustering for multi-type relational data
ICML '06 Proceedings of the 23rd international conference on Machine learning
Trust Region Newton Method for Logistic Regression
The Journal of Machine Learning Research
Reviewing and Evaluating Automatic Term Recognition Techniques
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Methodological Review: Formal representation of eligibility criteria: A literature review
Journal of Biomedical Informatics
A practical method for transforming free-text eligibility criteria into computable criteria
Journal of Biomedical Informatics
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
DTMBIO 2011: international workshop on data and textmining in biomedical informatics
Proceedings of the 20th ACM international conference on Information and knowledge management
Inferring appropriate eligibility criteria in clinical trial protocols without labeled data
Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
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Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols.