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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2015 International Conference on Computer Application Technologies, CCATS 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 126-131 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781467382113 |
| DOIs | |
| Publication status | Published - 2016 Jan 4 |
| Event | 1st International Conference on Computer Application Technologies, CCATS 2015 - Matsue, Japan Duration: 2015 Aug 31 → 2015 Sep 2 |
Other
| Other | 1st International Conference on Computer Application Technologies, CCATS 2015 |
|---|---|
| Country | Japan |
| City | Matsue |
| Period | 15/8/31 → 15/9/2 |
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Keywords
- evid
- holistic
- induction
- Kampo medicine
- knowledge discovery
- machinery learning
- medical data
- objectification
- prescription prediction
- prescription-syndrome correspondence
- robust prediction
- statistical analysis
- tacit knowledge
- traditional medicine
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
Cite this
Prescription Prediction towards Computer-Assisted Diagnosis for Kampo Medicine. / Mi, Xiaoyu; Ikeda, Hiroshi; Nakazawa, Fumihiko; Matsuoka, Hidetoshi; Kataoka, Erika; Hamaya, Satoshi; Tanaka, Tatsuo; Odaguchi, Hiroshi; Ishige, Tatsuya; Ito, Yuichi; Wakasugi, Akino; Kawanabe, Tadaaki; Sekine, Mariko; Hanawa, Toshihiko; Yamaguchi, Shinichi.
Proceedings - 2015 International Conference on Computer Application Technologies, CCATS 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 126-131 7372328.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Prescription Prediction towards Computer-Assisted Diagnosis for Kampo Medicine
AU - Mi, Xiaoyu
AU - Ikeda, Hiroshi
AU - Nakazawa, Fumihiko
AU - Matsuoka, Hidetoshi
AU - Kataoka, Erika
AU - Hamaya, Satoshi
AU - Tanaka, Tatsuo
AU - Odaguchi, Hiroshi
AU - Ishige, Tatsuya
AU - Ito, Yuichi
AU - Wakasugi, Akino
AU - Kawanabe, Tadaaki
AU - Sekine, Mariko
AU - Hanawa, Toshihiko
AU - Yamaguchi, Shinichi
PY - 2016/1/4
Y1 - 2016/1/4
N2 - 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.
AB - 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.
KW - evid
KW - holistic
KW - induction
KW - Kampo medicine
KW - knowledge discovery
KW - machinery learning
KW - medical data
KW - objectification
KW - prescription prediction
KW - prescription-syndrome correspondence
KW - robust prediction
KW - statistical analysis
KW - tacit knowledge
KW - traditional medicine
UR - http://www.scopus.com/inward/record.url?scp=84964721804&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964721804&partnerID=8YFLogxK
U2 - 10.1109/CCATS.2015.38
DO - 10.1109/CCATS.2015.38
M3 - Conference contribution
AN - SCOPUS:84964721804
SP - 126
EP - 131
BT - Proceedings - 2015 International Conference on Computer Application Technologies, CCATS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -