TEXT2TABLE: medical text summarization system based on named entity recognition and modality identification

  • Aramaki E
  • Miura Y
  • Tonoike M
 et al. 
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With the rapidly growing use of electronic health records, the possibility of large-scale clinical information extraction has drawn much attention. It is not, however, easy to extract information because these reports are written in natural language. To address this problem, this paper presents a system that converts a medical text into a table structure. This system's core technologies are (1) medical event recognition modules and (2) a negative event identification module that judges whether an event actually occurred or not. Regarding the latter module, this paper also proposes an SVM-based classifier using syntactic information. Experimental results demonstrate empirically that syntactic information can contribute to the method's accuracy.

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  • Eiji Aramaki

  • Yasuhide Miura

  • Masatsugu Tonoike

  • Tomoko Ohkuma

  • Hiroshi Mashuichi

  • Kazuhiko Ohe

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