Nanaka Asai, Chiaki Doi, Koki Iwai, S. Ideno, H. Seki, Jungo Kato, Takashige Yamada, H. Morisaki, H. Shigeno
{"title":"利用电子麻醉记录避免诱导后低血压的麻醉剂量预测模型的提出","authors":"Nanaka Asai, Chiaki Doi, Koki Iwai, S. Ideno, H. Seki, Jungo Kato, Takashige Yamada, H. Morisaki, H. Shigeno","doi":"10.23919/ICMU48249.2019.9006672","DOIUrl":null,"url":null,"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.","PeriodicalId":348402,"journal":{"name":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"9 12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Proposal of Anesthetic Dose Prediction Model to Avoid Post-induction Hypotension Using Electronic Anesthesia Records\",\"authors\":\"Nanaka Asai, Chiaki Doi, Koki Iwai, S. Ideno, H. Seki, Jungo Kato, Takashige Yamada, H. Morisaki, H. Shigeno\",\"doi\":\"10.23919/ICMU48249.2019.9006672\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":348402,\"journal\":{\"name\":\"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)\",\"volume\":\"9 12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/ICMU48249.2019.9006672\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ICMU48249.2019.9006672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Proposal of Anesthetic Dose Prediction Model to Avoid Post-induction Hypotension Using Electronic Anesthesia Records
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.