@inproceedings{f3ce8e26e6e14ef39174460a5b49bd17,
title = "Prescription Prediction towards Computer-Assisted Diagnosis for Kampo Medicine",
abstract = "This paper focuses on the attempt to formulate the prescription prediction logic based on the medical data analysis towards the future computer-assisted-diagnosis for Kampo medicine. We constructed and evaluated prediction models for some frequently-used prescriptions using six kinds of machine learning algorithms including artificial neural network, multinomial logit, random forest, support vector machine, k-nearest neighbor, and decision tree. The possibility of prescription prediction and the necessary amount of data required for robust prediction are clarified.",
keywords = "Kampo medicine, evid, holistic, induction, knowledge discovery, machinery learning, medical data, objectification, prescription prediction, prescription-syndrome correspondence, robust prediction, statistical analysis, tacit knowledge, traditional medicine",
author = "Xiaoyu Mi and Hiroshi Ikeda and Fumihiko Nakazawa and Hidetoshi Matsuoka and Erika Kataoka and Satoshi Hamaya and Tatsuo Tanaka and Hiroshi Odaguchi and Tatsuya Ishige and Yuichi Ito and Akino Wakasugi and Tadaaki Kawanabe and Mariko Sekine and Toshihiko Hanawa and Shinichi Yamaguchi",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 1st International Conference on Computer Application Technologies, CCATS 2015 ; Conference date: 31-08-2015 Through 02-09-2015",
year = "2016",
month = jan,
day = "4",
doi = "10.1109/CCATS.2015.38",
language = "English",
series = "Proceedings - 2015 International Conference on Computer Application Technologies, CCATS 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "126--131",
editor = "Antoine Bossard and Satoshi Takahashi and Yohei Shiraki and Toshiyuki Tanaka",
booktitle = "Proceedings - 2015 International Conference on Computer Application Technologies, CCATS 2015",
}