Proposal of Anesthetic Dose Prediction Model to Avoid Post-induction Hypotension Using Electronic Anesthesia Records

Nanaka Asai, Chiaki Doi, Koki Iwai, Satoshi Ideno, Hiroyuki Seki, Jungo Kato, Takashige Yamada, Hiroshi Morisaki, Hiroshi Shigeno

研究成果: Conference contribution

抄録

Post-induction hypotension frequently occurred after anesthesia induction. Avoiding post-induction hypotension is important as it is associated with postoperative adverse outcomes. Related studies have shown that the dose of anesthetic induction drugs affects the post-induction hypotension. The purpose of this study is to propose an anesthetic dose that does not cause post-induction hypotension according to the patient's condition. A model for predicting the optimal dose of an anesthetic induction drug is constructed using a regression model which is one of machine learning methods by focusing on electronic anesthesia records. The prediction coefficient of determination 0.5008 was achieved by adjusting the explanatory variables and parameters and using ridge regression.

本文言語English
ホスト出版物のタイトル2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9784907626419
DOI
出版ステータスPublished - 2019 11
イベント12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019 - Kathmandu, Nepal
継続期間: 2019 11 42019 11 6

出版物シリーズ

名前2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019

Conference

Conference12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019
CountryNepal
CityKathmandu
Period19/11/419/11/6

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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