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Embedded Word Representations for Rich Indexing: A Case Study for Medical Records

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Case-Based Reasoning Research and Development (ICCBR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11156))

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Abstract

Case indexing decisions must often confront the tradeoff between rich semantic indexing schemes, which provide effective retrieval at large indexing cost, and shallower indexing schemes, which enable low-cost indexing but may be less reliable. Indexing for textual case-based reasoning is often based on information retrieval approaches that minimize index acquisition cost but sacrifice semantic information. This paper presents JointEmbed, a method for automatically generating rich indices. JointEmbed automatically generates continuous vector space embeddings that implicitly capture semantic information, leveraging multiple knowledge sources such as free text cases and pre-existing knowledge graphs. JointEmbed generates effective indices by applying pTransR, a novel approach for modelling knowledge graphs, to encode and summarize contents of domain knowledge resources. JointEmbed is applied to the medical CBR task of retrieving relevant patient electronic health records, for which potential health consequences make retrieval quality paramount. An evaluation supports that JointEmbed outperforms previous methods.

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Notes

  1. 1.

    http://www.ncbi.nlm.nih.gov/pmc/tolls/openftlist.

  2. 2.

    https://clinicaltrials.gov.

  3. 3.

    https://www.ncbi.nlm.nih.gov/mesh.

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Acknowledgement

This work is supported by the Indiana University Precision Health Initiative.

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Authors and Affiliations

  1. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA

    Katherine Metcalf & David Leake

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  1. Katherine Metcalf
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  2. David Leake
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Correspondence to David Leake .

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Editors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Michael T. Cox

  2. Mälardalen University, Västeras, Sweden

    Peter Funk

  3. Mälardalen University, Västeras, Sweden

    Shahina Begum

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Metcalf, K., Leake, D. (2018). Embedded Word Representations for Rich Indexing: A Case Study for Medical Records. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_18

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  • DOI: https://doi.org/10.1007/978-3-030-01081-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01080-5

  • Online ISBN: 978-3-030-01081-2

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