TY - JOUR
T1 - Prediction of deficiency-excess pattern in Japanese Kampo medicine
T2 - Multi-centre data collection
AU - Maeda-Minami, Ayako
AU - Yoshino, Tetsuhiro
AU - Katayama, Kotoe
AU - Horiba, Yuko
AU - Hikiami, Hiroaki
AU - Shimada, Yutaka
AU - Namiki, Takao
AU - Tahara, Eiichi
AU - Minamizawa, Kiyoshi
AU - Muramatsu, Shinichi
AU - Yamaguchi, Rui
AU - Imoto, Seiya
AU - Miyano, Satoru
AU - Mima, Hideki
AU - Mimura, Masaru
AU - Nakamura, Tomonori
AU - Watanabe, Kenji
N1 - Funding Information:
This work was supported by a Grant-in-Aid for Research on Propulsion Study of Clinical Research from the Ministry of Health, Labour and Welfare in building the questionnaire, data collection, and analysis. The funding source had no involvement in the interpretation of data, writing of the report, and the decision to submit the article for publication.
Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
KW - Decision support system
KW - Machine learning
KW - The 11th version of the international classification of diseases (ICD-11)
KW - Traditional medicine pattern
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U2 - 10.1016/j.ctim.2019.07.003
DO - 10.1016/j.ctim.2019.07.003
M3 - Article
C2 - 31331566
AN - SCOPUS:85068613844
VL - 45
SP - 228
EP - 233
JO - Complementary Therapies in Medicine
JF - Complementary Therapies in Medicine
SN - 0965-2299
ER -