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 930-0194, 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. Division of System Analysis, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Nagoya Chikusa-ku, Aichi 464-8681, Japan. Electronic address: r.yamaguchi@aichi-cc.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@ims.u-tokyo.ac.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. Electronic address: watanabekenji@keio.jp.
Abstract
OBJECTIVE: The purpose of this study was to extract important patient questionnaire items by creating random forest models for predicting pattern diagnosis considering an interaction between deficiency-excess and cold-heat patterns. DESIGN: A multi-centre prospective observational study. SETTING: Participants visiting six Kampo speciality clinics in Japan from 2012 to 2015. MAIN OUTCOME MEASURE: Deficiency-excess pattern diagnosis made by board-certified Kampo experts. METHODS: We used 153 items as independent variables including, age, sex, body mass index, systolic and diastolic blood pressures, and 148 subjective symptoms recorded through a questionnaire. We sampled training data with an equal number of the different patterns from a 2 × 2 factorial combination of deficiency-excess and cold-heat patterns. We constructed the prediction models of deficiency-excess and cold-heat patterns using the random forest algorithm, extracted the top 10 essential items, and calculated the discriminant ratio using this prediction model. RESULTS: BMI and blood pressure, and subjective symptoms of cold or heat sensations were the most important items in the prediction models of deficiency-excess pattern and of cold-heat patterns, respectively. The discriminant ratio was not inferior compared with the result ignoring the interaction between the diagnoses. CONCLUSIONS: We revised deficiency-excess and cold-heat pattern prediction models, based on balanced training sample data obtained from six Kampo speciality clinics in Japan. The revised important items for diagnosing a deficiency-excess pattern and cold-heat pattern were compatible with the definition in the 11th version of international classification of diseases.
OBJECTIVE: The purpose of this study was to extract important patient questionnaire items by creating random forest models for predicting pattern diagnosis considering an interaction between deficiency-excess and cold-heat patterns. DESIGN: A multi-centre prospective observational study. SETTING:Participants visiting six Kampo speciality clinics in Japan from 2012 to 2015. MAIN OUTCOME MEASURE: Deficiency-excess pattern diagnosis made by board-certified Kampo experts. METHODS: We used 153 items as independent variables including, age, sex, body mass index, systolic and diastolic blood pressures, and 148 subjective symptoms recorded through a questionnaire. We sampled training data with an equal number of the different patterns from a 2 × 2 factorial combination of deficiency-excess and cold-heat patterns. We constructed the prediction models of deficiency-excess and cold-heat patterns using the random forest algorithm, extracted the top 10 essential items, and calculated the discriminant ratio using this prediction model. RESULTS: BMI and blood pressure, and subjective symptoms of cold or heat sensations were the most important items in the prediction models of deficiency-excess pattern and of cold-heat patterns, respectively. The discriminant ratio was not inferior compared with the result ignoring the interaction between the diagnoses. CONCLUSIONS: We revised deficiency-excess and cold-heat pattern prediction models, based on balanced training sample data obtained from six Kampo speciality clinics in Japan. The revised important items for diagnosing a deficiency-excess pattern and cold-heat pattern were compatible with the definition in the 11th version of international classification of diseases.