Machine learning model for predicting the cold-heat pattern in Kampo medicine: a multicenter prospective observational study.

Autor:	Maeda-Minami A; Faculty of Pharmaceutical Sciences, Tokyo University of Science, Chiba, Japan., Yoshino T; Center for Kampo Medicine, Keio University School of Medicine, Shinjuku, Tokyo, Japan., Katayama K; Human Genome Center, the Institute of Medical Science, University of Tokyo, Minato, Tokyo, Japan., Horiba Y; Center for Kampo Medicine, Keio University School of Medicine, Shinjuku, Tokyo, Japan., Hikiami H; Shikino Care Center, Toyama, Japan., Shimada Y; University of Toyama, Toyama, Japan., Namiki T; Department of Japanese Oriental (Kampo) Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan., Tahara E; Department of Kampo Medicine, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan., Minamizawa K; Department of Oriental Medicine, Kameda Medical Center, Chiba, Japan., Muramatsu SI; Division of Oriental Medicine, Center of Community Medicine, Jichi Medical University, Tochigi, Japan., Yamaguchi R; Human Genome Center, the Institute of Medical Science, University of Tokyo, Minato, Tokyo, Japan.; Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan., Imoto S; Human Genome Center, the Institute of Medical Science, University of Tokyo, Minato, Tokyo, Japan., Miyano S; Human Genome Center, the Institute of Medical Science, University of Tokyo, Minato, Tokyo, Japan.; Department of Integrated Analytics, M&D Data Science Center, Tokyo Medical and Dental University, Bunkyo, Tokyo, Japan., Mima H; Promoting Organization for Future Creators, Kyushu University, Fukuoka, Japan., Uneda K; Department of Kampo Medicine, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan., Nogami T; Department of Kampo Medicine, Tokai University School of Medicine, Isehara, Kanagawa, Japan., Fukunaga K; Center for Kampo Medicine, Keio University School of Medicine, Shinjuku, Tokyo, Japan.; Department of Medicine, Division of Pulmonary Medicine, Keio University School of Medicine, Shinjuku, Tokyo, Japan., Watanabe K; Center for Kampo Medicine, Keio University School of Medicine, Shinjuku, Tokyo, Japan.
Jazyk:	angličtina
Zdroj:	Frontiers in pharmacology [Front Pharmacol] 2024 Oct 25; Vol. 15, pp. 1412593. Date of Electronic Publication: 2024 Oct 25 (Print Publication: 2024).
DOI:	10.3389/fphar.2024.1412593
Abstrakt:	Objective: The purpose of this study was to predict the four cold-heat patterns in patients who have the subjective symptoms of the cold-heat pattern described in the International Classification of Diseases Traditional Medicine Conditions - Module 1 by applying a machine learning algorithm. Methods: Subjects were first-visit Kampo outpatients at six institutions who agreed to participate in this multicenter prospective observational study. The cold pattern model and the heat pattern model were created separately with 148 symptoms, body mass index, blood pressure (systolic and diastolic), age, and sex. Along with a single cold or heat pattern, the tangled heat/cold pattern is defined as being predicted by both cold and heat patterns, while the moderate (heat/cold) pattern is defined as being predicted by neither the cold pattern nor the heat pattern. Results: We included 622 participants (mean age ±standard deviation, 54.4 ± 16.9; with female 501). The accuracy, macro-recall, precision, and F1-score of a combination of the two prediction models were 96.7%, 93.2%, 85.6%, and 88.5% respectively. The important items were compatible with the definitions of the cold-heat pattern. Conclusion: We developed a prediction model on cold-heat patterns with data from patients whose subjective cold/heat-related symptoms matched the cold-heat pattern diagnosis by the physician. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (Copyright © 2024 Maeda-Minami, Yoshino, Katayama, Horiba, Hikiami, Shimada, Namiki, Tahara, Minamizawa, Muramatsu, Yamaguchi, Imoto, Miyano, Mima, Uneda, Nogami, Fukunaga and Watanabe.)
Databáze:	MEDLINE
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