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Automatic Decision Support for Clinical Diagnostic Literature Using Link Analysis in a Weighted Keyword Network

  • Systems-level quality improvement
  • Published:
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Abstract

We present a novel approach to recommending articles from the medical literature that support clinical diagnostic decision-making, giving detailed descriptions of the associated ideas and principles. The specific goal is to retrieve biomedical articles that help answer questions of a specified type about a particular case. Based on the filtered keywords, MeSH(Medical Subject Headings) lexicon and the automatically extracted acronyms, the relationship between keywords and articles was built. The paper gives a detailed description of the process of by which keywords were measured and relevant articles identified based on link analysis in a weighted keywords network. Some important challenges identified in this study include the extraction of diagnosis-related keywords and a collection of valid sentences based on the keyword co-occurrence analysis and existing descriptions of symptoms. All data were taken from medical articles provided in the TREC (Text Retrieval Conference) clinical decision support track 2015. Ten standard topics and one demonstration topic were tested. In each case, a maximum of five articles with the highest relevance were returned. The total user satisfaction of 3.98 was 33% higher than average. The results also suggested that the smaller the number of results, the higher the average satisfaction. However, a few shortcomings were also revealed since medical literature recommendation for clinical diagnostic decision support is so complex a topic that it cannot be fully addressed through the semantic information carried solely by keywords in existing descriptions of symptoms. Nevertheless, the fact that these articles are actually relevant will no doubt inspire future research.

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Acknowledgements

This work was supported by the Chinese National Social Science Foundation 16BTQ030 (2016).

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Authors and Affiliations

  1. College of Information Engineering, Nanjing University of Finance & Economics, Nanjing, 210023, China

    Shuqing Li

  2. Department of Library and Information Studies, Graduate School of Education, University at Buffalo, New York State University, Buffalo, NY, 14260, USA

    Ying Sun & Dagobert Soergel

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  1. Shuqing Li
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  2. Ying Sun
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  3. Dagobert Soergel
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Correspondence to Shuqing Li.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Li, S., Sun, Y. & Soergel, D. Automatic Decision Support for Clinical Diagnostic Literature Using Link Analysis in a Weighted Keyword Network. J Med Syst 42, 27 (2018). https://doi.org/10.1007/s10916-017-0876-3

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