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A Lightweight Approach for Extracting Disease-Symptom Relation with MetaMap toward Automated Generation of Disease Knowledge Base

  • Takashi Okumura
  • Yuka Tateisi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7231)

Abstract

Diagnostic decision support systems necessitate disease knowledge base, and this part may occupy dominant portion in the total development cost of such systems. Accordingly, toward automated generation of disease knowledge base, we conducted a preliminary study for efficient extraction of symptomatic expressions, utilizing MetaMap, a tool for assigning UMLS (Unified Medical Language System) semantic tags onto phrases in a given medical literature text.

We first utilized several tags in the MetaMap output, related to symptoms and findings, for extraction of symptomatic terms. This straightforward approach resulted in Recall 82% and Precision 64%. Then, we applied a heuristics that exploits certain patterns of tag sequences that frequently appear in typical symptomatic expressions. This simple approach achieved 7% recall gain, without sacrificing precision.

Although the extracted information requires manual inspection, the study suggested that the simple approach can extract symptomatic expressions, at very low cost. Failure analysis of the output was also performed to further improve the performance.

Keywords

Symptomatic Expression Free Text Format Lightweight Approach Clinical Synopsis Diagnostic Decision Support System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Takashi Okumura
    • 1
  • Yuka Tateisi
    • 1
  1. 1.National Institute of Public HealthSaitamaJapan

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