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

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

Abstract

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.

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

  • machine learning
  • medical
  • stacking

ASJC Scopus subject areas

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

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  • Cite this

    Iwai, K., Doi, C., Asai, N., Shigeno, H., Ideno, S., Kato, J., Yamada, T., Morisaki, H., & Seki, H. (2019). Prediction of Post-induction Hypotension Using Stacking Method. In 2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019 [9006639] (2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ICMU48249.2019.9006639