Abstract
In an actual electronic health record (EHR), patient notes are written with terse language and clinical jargons. However, most Pseudo Relevance Feedback (PRF) technique methods do not take into account the significant degree of candidate term in feedback documents and the co-occurrence relationship between a candidate term and a query term simultaneously. In this paper, we study how to incorporate proximity information into the Rocchio’s model, and propose a HAL-based Rocchio’s model, called HRoc. A new concept of term proximity feedback weight is introduced to model in the query expansion. Then, we propose three normalization methods to incorporate proximity information. Experimental results on 2016 TREC Clinical Support Medicine collections show that our proposed models are effective and generally superior to the state-of-the-art relevance feedback models.
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Acknowledgement
The National Natural Science Foundation of China (61532008), the National Key Research and Development Program of China (2017YFC0909502) support this research.
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Pan, M., Zhang, Y., He, T., Jiang, X. (2018). An Enhanced HAL-Based Pseudo Relevance Feedback Model in Clinical Decision Support Retrieval. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_12
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DOI: https://doi.org/10.1007/978-3-319-95933-7_12
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