Format

Send to

Choose Destination
Complement Ther Med. 2019 Aug;45:228-233. doi: 10.1016/j.ctim.2019.07.003. Epub 2019 Jul 5.

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

Author information

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.

KEYWORDS:

Decision support system; Machine learning; The 11th version of the international classification of diseases (ICD-11); Traditional medicine pattern ((TM1))

PMID:
31331566
DOI:
10.1016/j.ctim.2019.07.003

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center