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PERSIVAL, a system for personalized search and summarization over multimedia healthcare information
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
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Proceedings of the 7th international conference on Intelligent user interfaces
Accessing heterogenous sources of evidence to answer clinical questions
Computers and Biomedical Research
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Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Personalizing search via automated analysis of interests and activities
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Answer extraction, semantic clustering, and extractive summarization for clinical question answering
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Answering Clinical Questions with Knowledge-Based and Statistical Techniques
Computational Linguistics
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Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
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Generative content models for structural analysis of medical abstracts
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SumQA '06 Proceedings of the Workshop on Task-Focused Summarization and Question Answering
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International Journal of Information Management: The Journal for Information Professionals
Automated user modeling for personalized digital libraries
International Journal of Information Management: The Journal for Information Professionals
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Despite the large amount of online medical literature, it can be difficult for clinicians to find relevant information at the point of patient care. In this paper, we present techniques to personalize the results of search, making use of the online patient record as a sophisticated, pre-existing user model. Our work in PERSIVAL, a medical digital library, includes methods for re-ranking the results of search to prioritize those that better match the patient record. It also generates summaries of the re-ranked results which highlight information that is relevant to the patient under the physician's care. We focus on the use of a common representation for the articles returned by search and the patient record which facilitates both the re-ranking and the summarization tasks. This common approach to both tasks has a strong positive effect on the ability to personalize information.