Ayako Maeda-Minami1, Tetsuhiro Yoshino2, Kotoe Katayama3, Yuko Horiba4, Hiroaki Hikiami5, Yutaka Shimada6, Takao Namiki7, Eiichi Tahara8, Kiyoshi Minamizawa9, Shinichi Muramatsu10, Rui Yamaguchi11, Seiya Imoto12, Satoru Miyano13, Hideki Mima14, Masaru Mimura15, Tomonori Nakamura16, Kenji Watanabe17. 1. Division of Pharmaceutical Care Sciences, Graduate School of Pharmacy, Keio University, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan. Electronic address: ayako373@keio.jp. 2. Center for Kampo Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: tetta213@keio.jp. 3. Human Genome Center, The Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. Electronic address: k-kataya@hgc.jp. 4. Center for Kampo Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: mannta217@keio.jp. 5. Shikino Care Center, 480 Washikitashin, Takaoka, Toyama, 933-0071, Japan. Electronic address: hhikiami1327@gmail.com. 6. Department of Japanese Oriental (Kampo) Medicine, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, 2630 Sugitani, Toyama, 930-7587, Japan. Electronic address: shimada@med.u-toyama.ac.jp. 7. Department of Japanese Oriental (Kampo) Medicine, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, Chiba, 260-8760, Japan. Electronic address: tnamiki@faculty.chiba-u.jp. 8. Department of Japanese Oriental (Kampo) Medicine, Oriental Medical Center, Iizuka Hospital, 3-83 Yoshio-cho, Iizuka, Fukuoka, 920-8505, Japan. Electronic address: etaharah1@aih-net.com. 9. Department of Oriental Medicine, Kameda Medical Center, 929 Higashi-cho, Kamogawa, Chiba, 296-8602, Japan. Electronic address: k-mnmzw@kameda.jp. 10. Division of Oriental Medicine, Center of Community Medicine, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi, 329-0498, Japan. Electronic address: muramats@ms2.jichi.ac.jp. 11. Human Genome Center, The Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. Electronic address: ruiy@hgc.jp. 12. Division of Health Medical Data Science, Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. Electronic address: imoto@hgc.jp. 13. Human Genome Center, The Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. Electronic address: miyano@hgc.jp. 14. Center for Research and Development of Higher Education, University of Tokyo, 7-3-1 Hongou, Bunkyo-ku, Tokyo, 113-0033, Japan. Electronic address: mima@he.u-tokyo.ac.jp. 15. Center for Kampo Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: mimura@keio.jp. 16. Division of Pharmaceutical Care Sciences, Graduate School of Pharmacy, Keio University, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan. Electronic address: nakamura-tm@pha.keio.ac.jp. 17. Center for Kampo Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan; Faculty of Environmental and Information Study, Keio University, 5322 Endo, Fujisawa, Kanagawa, 252-0882, Japan. Electronic address: watanabekenji@keio.jp.
Abstract
OBJECTIVE: The purpose of the present study was to compare important patient questionnaire items by creating a random forest model for predicting deficiency-excess pattern diagnosis in six Kampo specialty clinics. DESIGN: A multi-centre prospective observational study. SETTING: Participants who visited six Kampo specialty clinics in Japan from 2012 to 2015. MAIN OUTCOME MEASURE: Deficiency-excess pattern diagnosis made by board-certified Kampo experts. METHODS: To predict the deficiency-excess pattern diagnosis by Kampo experts, we used 153 items as independent variables, namely, age, sex, body mass index, systolic and diastolic blood pressures, and 148 subjective symptoms recorded through a questionnaire. We extracted the 30 most important items in each clinic's random forest model and selected items that were common among the clinics. We integrated participating clinics' data to construct a prediction model in the same manner. We calculated the discriminant ratio using this prediction model for the total six clinics' data and each clinic's independent data. RESULTS: Fifteen items were commonly listed in top 30 items in each random forest model. The discriminant ratio of the total six clinics' data was 82.3%; moreover, with the exception of one clinic, the independent discriminant ratio of each clinic was approximately 80% each. CONCLUSIONS: We identified common important items in diagnosing a deficiency-excess pattern among six Japanese Kampo clinics. We constructed the integrated prediction model of deficiency-excess pattern.
OBJECTIVE: The purpose of the present study was to compare important patient questionnaire items by creating a random forest model for predicting deficiency-excess pattern diagnosis in six Kampo specialty clinics. DESIGN: A multi-centre prospective observational study. SETTING:Participants who visited six Kampo specialty clinics in Japan from 2012 to 2015. MAIN OUTCOME MEASURE: Deficiency-excess pattern diagnosis made by board-certified Kampo experts. METHODS: To predict the deficiency-excess pattern diagnosis by Kampo experts, we used 153 items as independent variables, namely, age, sex, body mass index, systolic and diastolic blood pressures, and 148 subjective symptoms recorded through a questionnaire. We extracted the 30 most important items in each clinic's random forest model and selected items that were common among the clinics. We integrated participating clinics' data to construct a prediction model in the same manner. We calculated the discriminant ratio using this prediction model for the total six clinics' data and each clinic's independent data. RESULTS: Fifteen items were commonly listed in top 30 items in each random forest model. The discriminant ratio of the total six clinics' data was 82.3%; moreover, with the exception of one clinic, the independent discriminant ratio of each clinic was approximately 80% each. CONCLUSIONS: We identified common important items in diagnosing a deficiency-excess pattern among six Japanese Kampo clinics. We constructed the integrated prediction model of deficiency-excess pattern.
Keywords:
Decision support system; Machine learning; The 11th version of the international classification of diseases (ICD-11); Traditional medicine pattern ((TM1))