Proposal of Anesthetic Dose Prediction Model to Avoid Post-induction Hypotension Using Electronic Anesthesia Records
- Resource Type
- Conference
- Authors
- Asai, Nanaka; Doi, Chiaki; Iwai, Koki; Ideno, Satoshi; Seki, Hiroyuki; Kato, Jungo; Yamada, Takashige; Morisaki, Hiroshi; Shigeno, Hiroshi
- Source
- 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU) Mobile Computing and Ubiquitous Network (ICMU), 2019 Twelfth International Conference on. :1-4 Nov, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Anesthesia
Predictive models
Drugs
Blood pressure
Feature extraction
Correlation
Machine learning
machine learning
prediction model
regression
anesthesia
data mining
medical
- Language
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.