@inproceedings{402834aa19fb4b7d9d4a69de56564e16,
title = "Proposal of Anesthetic Dose Prediction Model to Avoid Post-induction Hypotension Using Electronic Anesthesia Records",
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.",
keywords = "anesthesia, data mining, machine learning, medical, prediction model, regression",
author = "Nanaka Asai and Chiaki Doi and Koki Iwai and Satoshi Ideno and Hiroyuki Seki and Jungo Kato and Takashige Yamada and Hiroshi Morisaki and Hiroshi Shigeno",
note = "Publisher Copyright: {\textcopyright} 2019 IPSJ.; 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019 ; Conference date: 04-11-2019 Through 06-11-2019",
year = "2019",
month = nov,
doi = "10.23919/ICMU48249.2019.9006672",
language = "English",
series = "2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019",
}