Prediction of deficiency-excess pattern in Japanese Kampo medicine

Multi-centre data collection

Ayako Maeda-Minami, Tetsuhiro Yoshino, Kotoe Katayama, Yuko Horiba, Hiroaki Hikiami, Yutaka Shimada, Takao Namiki, Eiichi Tahara, Kiyoshi Minamizawa, Shinichi Muramatsu, Rui Yamaguchi, Seiya Imoto, Satoru Miyano, Hideki Mima, Masaru Mimura, Tomonori Nakamura, Kenji Watanabe

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)228-233
Number of pages6
JournalComplementary Therapies in Medicine
Volume45
DOIs
Publication statusPublished - 2019 Aug 1

Fingerprint

Kampo Medicine
Blood Pressure
Observational Studies
Japan
Body Mass Index
Outcome Assessment (Health Care)
Prospective Studies
Forests

Keywords

  • Decision support system
  • Machine learning
  • The 11th version of the international classification of diseases (ICD-11)
  • Traditional medicine pattern

ASJC Scopus subject areas

  • Complementary and Manual Therapy
  • Complementary and alternative medicine
  • Advanced and Specialised Nursing

Cite this

Prediction of deficiency-excess pattern in Japanese Kampo medicine : Multi-centre data collection. / Maeda-Minami, Ayako; Yoshino, Tetsuhiro; Katayama, Kotoe; Horiba, Yuko; Hikiami, Hiroaki; Shimada, Yutaka; Namiki, Takao; Tahara, Eiichi; Minamizawa, Kiyoshi; Muramatsu, Shinichi; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru; Mima, Hideki; Mimura, Masaru; Nakamura, Tomonori; Watanabe, Kenji.

In: Complementary Therapies in Medicine, Vol. 45, 01.08.2019, p. 228-233.

Research output: Contribution to journalArticle

Maeda-Minami, A, Yoshino, T, Katayama, K, Horiba, Y, Hikiami, H, Shimada, Y, Namiki, T, Tahara, E, Minamizawa, K, Muramatsu, S, Yamaguchi, R, Imoto, S, Miyano, S, Mima, H, Mimura, M, Nakamura, T & Watanabe, K 2019, 'Prediction of deficiency-excess pattern in Japanese Kampo medicine: Multi-centre data collection', Complementary Therapies in Medicine, vol. 45, pp. 228-233. https://doi.org/10.1016/j.ctim.2019.07.003
Maeda-Minami, Ayako ; Yoshino, Tetsuhiro ; Katayama, Kotoe ; Horiba, Yuko ; Hikiami, Hiroaki ; Shimada, Yutaka ; Namiki, Takao ; Tahara, Eiichi ; Minamizawa, Kiyoshi ; Muramatsu, Shinichi ; Yamaguchi, Rui ; Imoto, Seiya ; Miyano, Satoru ; Mima, Hideki ; Mimura, Masaru ; Nakamura, Tomonori ; Watanabe, Kenji. / Prediction of deficiency-excess pattern in Japanese Kampo medicine : Multi-centre data collection. In: Complementary Therapies in Medicine. 2019 ; Vol. 45. pp. 228-233.
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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.",
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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

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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.

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