BMC Medical Informatics and Decision Making (Jul 2023)

Development of a novel drug information provision system for Kampo medicine using natural language processing technology

  • Ayako Maeda-Minami,
  • Tetsuhiro Yoshino,
  • Tetsuro Yumoto,
  • Kayoko Sato,
  • Atsunobu Sagara,
  • Kenjiro Inaba,
  • Hidenori Kominato,
  • Takao Kimura,
  • Tetsuya Takishita,
  • Gen Watanabe,
  • Tomonori Nakamura,
  • Yasunari Mano,
  • Yuko Horiba,
  • Kenji Watanabe,
  • Junzo Kamei

DOI
https://doi.org/10.1186/s12911-023-02230-3
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 12

Abstract

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Abstract Background Kampo medicine is widely used in Japan; however, most physicians and pharmacists have insufficient knowledge and experience in it. Although a chatbot-style system using machine learning and natural language processing has been used in some clinical settings and proven useful, the system developed specifically for the Japanese language using this method has not been validated by research. The purpose of this study is to develop a novel drug information provision system for Kampo medicines using a natural language classifier® (NLC®) based on IBM Watson. Methods The target Kampo formulas were 33 formulas listed in the 17th revision of the Japanese Pharmacopoeia. The information included in the system comes from the package inserts of Kampo medicines, Manuals for Management of Individual Serious Adverse Drug Reactions, and data on off-label usage. The system developed in this study classifies questions about the drug information of Kampo formulas input by natural language into preset questions and outputs preset answers for the questions. The system uses morphological analysis, synonym conversion by thesaurus, and NLC®. We fine-tuned the information registered into NLC® and increased the thesaurus. To validate the system, 900 validation questions were provided by six pharmacists who were classified into high or low levels of knowledge and experience of Kampo medicines and three pharmacy students. Results The precision, recall, and F-measure of the system performance were 0.986, 0.915, and 0.949, respectively. The results were stable even with differences in the amount of expertise of the question authors. Conclusions We developed a system using natural language classification that can give appropriate answers to most of the validation questions.

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