Presentation 2020-02-14
Estimation of Target Plasma Concentration of Propofol by Decision Tree Learning and Polynomial Modeling
Kei Ten, Eisaka Toshio,
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Abstract(in English) In total intravenous anesthesia surgery, an anesthetic administration system that adjusts the drug concentration in blood is useful. TCI, a typical anesthesia support system, is an open-loop control system that predicts drug concentration using a compartment model obtained from pharmacokinetics. TCI has been put to practical use and has helped to reduce the burden on anesthesiologists, but its accuracy is not sufficient, and it is essential to use it together with an EEG monitor. In this paper, we propose a new method for estimating an appropriate target sedative blood concentration from the blood pressure and heart rate of an intraoperative patient using decision tree learning and a polynomial model.
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Paper # AI2019-45
Date of Issue 2020-02-07 (AI)

Conference Information
Committee AI
Conference Date 2020/2/14(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Izumo Campus, Shimane University
Topics (in Japanese) (See Japanese page)
Topics (in English) Socionetwork Strategies in the Market of Data VI: Cross-cultural Collaboration and Life Space Innovation, etc.
Chair Naoki Fukuta(Shizuoka Univ.)
Vice Chair Yuichi Sei(Univ. of Electro-Comm.) / Yuko Sakurai(AIST)
Secretary Yuichi Sei(Osaka Univ.) / Yuko Sakurai(Tokyo Univ. of Agriculture and Technology)
Assistant

Paper Information
Registration To Technical Committee on Artificial Intelligence and Knowledge-Based Processing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Estimation of Target Plasma Concentration of Propofol by Decision Tree Learning and Polynomial Modeling
Sub Title (in English)
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1st Author's Name Kei Ten
1st Author's Affiliation Kitami Institute of Technology(Kitami Inst. of Tech.)
2nd Author's Name Eisaka Toshio
2nd Author's Affiliation Kitami Institute of Technology(Kitami Inst. of Tech.)
Date 2020-02-14
Paper # AI2019-45
Volume (vol) vol.119
Number (no) AI-413
Page pp.pp.15-16(AI),
#Pages 2
Date of Issue 2020-02-07 (AI)