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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9784907626419
DOIs
Publication statusPublished - 2019 Nov
Event12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019 - Kathmandu, Nepal
Duration: 2019 Nov 42019 Nov 6

Publication series

Name2019 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

Keywords

  • anesthesia
  • data mining
  • machine learning
  • medical
  • prediction model
  • regression

ASJC Scopus subject areas

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

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  • Cite this

    Asai, N., Doi, C., Iwai, K., Ideno, S., Seki, H., Kato, J., Yamada, T., Morisaki, H., & Shigeno, H. (2019). Proposal of Anesthetic Dose Prediction Model to Avoid Post-induction Hypotension Using Electronic Anesthesia Records. In 2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019 [9006672] (2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ICMU48249.2019.9006672