Abstract
The goal of Clinical Decision Support (CDS) is to help physicians find useful information from a collection of medical articles with respect to the given patient records, in order to take the best care of their patients. Most of the existing CDS methods do not sufficiently consider the semantic evidence, hence the potential in improving the performance in biomedical articles retrieval. This paper proposes a novel feedback-based approach which considers the semantic association between a retrieved biomedical article and a pseudo feedback set. Evaluation results show that our method outperforms the strong baselines, and is able to improve over the best runs in the CDS tasks of TREC 2014 & 2015.
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Notes
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The learned embeddings of words and biomedical articles can be downloaded from http://gucasir.org/CDS.tgz.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61472391). We would like to thank the authors of [3] for kindly sharing their TREC runs with us.
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Yang, C., He, B., Xu, J. (2017). Integrating Feedback-Based Semantic Evidence to Enhance Retrieval Effectiveness for Clinical Decision Support. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_13
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