Prediction of Post-induction Hypotension Using Stacking Method

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

研究成果: Conference contribution

抄録

Electronic anesthesia record data have been accumulated, and efforts to solve medical problems using data analysis methods and machine learning have been conducted. Post-induction hypotension frequently occurred after induction of anesthesia. Intraoperative hypotension is associated with various adverse events such as myocardial infarction and cerebral infarction. In a related study, eight machine learning methods were used to construct hypotension prediction models and evaluated by area under the curve (AUC), using data collected from an institution in the United States. Nevertheless, it was not focused on improving prediction power. This paper aims to predict post-induction hypotension with high prediction power using 1,626 electronic anesthesia record data. Our hypotension prediction model using a stacking method is introduced. F-measure 0.60 was achieved by using our method through the evaluation.

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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