Reports from University of Tokyo Add New Data to Findings in Machine Learning (Machine Learning-based Models for Outpatient Prescription of Kampo Formulations: an Analysis of a Health Insurance Claims Database): Machine Learning

2023 APR 10 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Current study results on Machine Learning have been published. According to news reporting originating from Tokyo, Japan, by NewsRx correspondents, research stated, “Despite the widespread practice of Japanese traditional Kampo medicine, the characteristics of patients receiving various Kampo formulations have not been documented in detail. We applied a machine learning model to a health insurance claims database to identify the factors associated with the use of Kampo formulations.”

Financial supporters for this research include Ministry of Health, Labour and Welfare, Japan, Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT), Japan Kampo Medicines Manufacturers Association.

Our news editors obtained a quote from the research from the University of Tokyo, “A ten-percent sample of enrollees of the JMDC Claims Database in 2018 and 2019 was used to create the training and testing sets, respectively. Logistic regression with lasso regularization were performed in the training set to construct models with prescriptions of 10 commonly used Kampo formulations in one year as the dependent variable and data of the preceding year as independent variables. Models were applied to the testing set to calculate the C-statistics. Additionally, the performance of simplified scores using 10 or 5 variables were evaluated. There were 338,924 and 399,174 enrollees in the training and testing sets, respectively. The commonly prescribed Kampo formulations included kakkonto, bakumondoto, and shoseityuto. Based on the lasso models, the C-statistics ranged from 0.643 (maoto) to 0.888 (tokishakuyakusan). The models identified both the common determinants of different Kampo formulations and the specific characteristics associated with particular Kampo formulations. The simplified scores were slightly inferior to full models. Lasso regression models showed good performance for explaining various Kampo prescriptions from claims data.”

According to the news editors, the research concluded: “The models identified the characteristics associated with Kampo formulation use.”

For more information on this research see: Machine Learning-based Models for Outpatient Prescription of Kampo Formulations: an Analysis of a Health Insurance Claims Database. Journal of Epidemiology, 2023. Journal of Epidemiology can be contacted at: Japan Epidemiological Assoc, Hongo Mt Bldg, 4 Fl, 7-2-2, Hongo, Bunkyo-Ku, Tokyo, Japan.

The news editors report that additional information may be obtained by contacting Hayato Yamana, University of Tokyo, Graduate School of Medicine, Dept. of Health Services Research, Hongo 7-3-1, Tokyo, Japan. Additional authors for this research include Nobuaki Michihata, Taisuke Jo, Akira Okada, Sachiko Ono and Hideo Yasunaga.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.2188/jea.JE20220089. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

(Our reports deliver fact-based news of research and discoveries from around the world.)