Prediction of deficiency-excess pattern in Japanese Kampo medicine: Multi-centre data collection

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Publisher: Elsevier B.V.
Document Type: Report
Length: 638 words

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Keywords The 11th version of the international classification of diseases (ICD-11); Traditional medicine pattern (TM1); Machine learning; Decision support system Highlights * Diagnosing the deficiency-excess pattern is important for choosing an appropriate herbal formula. * We collected the patients' subjective symptoms on e-questionnaire and extracted the important items in each facility's random forest model. * We identified common important items in diagnosing a deficiency-excess pattern in patients from six centres providing Japanese Kampo medicine. * The body mass index and systolic and diastolic blood pressure values were important items predicting the deficiency-excess pattern diagnosis. 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. Abbreviations ICD, international classification of diseases; VAS, visual analogue scale; BMI, body mass index Author Affiliation: (a) Division of Pharmaceutical Care Sciences, Graduate School of Pharmacy, Keio University, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan (b) Center for Kampo Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan (c) Human Genome Center, The Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan (d) Shikino Care Center, 480 Washikitashin, Takaoka, Toyama, 933-0071, Japan (e) Department of Japanese Oriental (Kampo) Medicine, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, 2630 Sugitani, Toyama, 930-7587, Japan (f) Department of Japanese Oriental (Kampo) Medicine, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, Chiba, 260-8760, Japan (g) Department of Japanese Oriental (Kampo) Medicine, Oriental Medical Center, Iizuka Hospital, 3-83 Yoshio-cho, Iizuka, Fukuoka, 920-8505, Japan (h) Department of Oriental Medicine, Kameda Medical Center, 929 Higashi-cho, Kamogawa, Chiba, 296-8602, Japan (i) Division of Oriental Medicine, Center of Community Medicine, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi, 329-0498, Japan (j) 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 (k) Center for Research and Development of Higher Education, University of Tokyo, 7-3-1 Hongou, Bunkyo-ku, Tokyo, 113-0033, Japan (l) Faculty of Environmental and Information Study, Keio University, 5322 Endo, Fujisawa, Kanagawa, 252-0882, Japan * Corresponding author at: 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Article History: Received 21 March 2019; Revised 2 June 2019; Accepted 1 July 2019 Byline: Ayako Maeda-Minami [ayako373@keio.jp] (a), Tetsuhiro Yoshino [tetta213@keio.jp] (b), Kotoe Katayama [k-kataya@hgc.jp] (c), Yuko Horiba [mannta217@keio.jp] (b), Hiroaki Hikiami [hhikiami1327@gmail.com] (d), Yutaka Shimada [shimada@med.u-toyama.ac.jp] (e), Takao Namiki [tnamiki@faculty.chiba-u.jp] (f), Eiichi Tahara [etaharah1@aih-net.com] (g), Kiyoshi Minamizawa [k-mnmzw@kameda.jp] (h), Shinichi Muramatsu [muramats@ms2.jichi.ac.jp] (i), Rui Yamaguchi [ruiy@hgc.jp] (c), Seiya Imoto [imoto@hgc.jp] (j), Satoru Miyano [miyano@hgc.jp] (c), Hideki Mima [mima@he.u-tokyo.ac.jp] (k), Masaru Mimura [mimura@keio.jp] (b), Tomonori Nakamura [nakamura-tm@pha.keio.ac.jp] (a), Kenji Watanabe [watanabekenji@keio.jp] (b,l,*)

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Gale Document Number: GALE|A593922344