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

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 sp...

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Bibliographic Details
Published in:	BMC medical informatics and decision making Vol. 23; no. 1; p. 119
Main Authors:	Maeda-Minami, Ayako, Yoshino, Tetsuhiro, Yumoto, Tetsuro, Sato, Kayoko, Sagara, Atsunobu, Inaba, Kenjiro, Kominato, Hidenori, Kimura, Takao, Takishita, Tetsuya, Watanabe, Gen, Nakamura, Tomonori, Mano, Yasunari, Horiba, Yuko, Watanabe, Kenji, Kamei, Junzo
Format:	Journal Article
Language:	English
Published:	England BioMed Central Ltd 13-07-2023 BioMed Central BMC
Subjects:	Adverse and side effects Chatbots Computational linguistics Conversational agents Coronaviruses COVID-19 Drug information provision system Drug interactions Drug stores Drugs Health aspects Humans Information sources Information systems Inserts Japan Japanese language Kampo medicine Language Language processing Machine learning Medicine Medicine, Botanic Medicine, Herbal Medicine, Kampo Methods Morphology Natural language interfaces Natural Language Processing Pharmacists Physicians Practice Question-answering Questions Side effects Technology Thesauri Japan Chatbots Natural language processing Kampo medicine Conversational agents Drug information provision system Question-answering
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Summary:	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. 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. 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. We developed a system using natural language classification that can give appropriate answers to most of the validation questions.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1472-6947 1472-6947
DOI:	10.1186/s12911-023-02230-3