Prediction of Mechanical Ventilator Weaning Outcome - A Deep Learning Approach

Kuei Hung Shen*, Yun Ju Yu, Szu Yin Chen, En Ming Chang, Hsiu Li Wu, Cheng Kuan Lin, Yu Chee Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Mechanical Ventilation (MV) is a type of therapy that helps patients breathe or breathes for patients when they can't breathe on their own. Doctors and therapists determine whether a patient is ready for weaning of mechanical ventilation based on vitals and various medical test results. This study explores deep learning methods applied to prediction of mechanical ventilator weaning outcome using multiple physiological parameters. Our experiments showed a validation accuracy of 0.682 with limited samples and less features compared to similar studies. We expect to see improved performance with more data and features collected.

Original languageEnglish
Title of host publicationProceedings - 2023 VTS Asia Pacific Wireless Communications Symposium, APWCS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350316803
DOIs
StatePublished - 2023
Event2023 VTS Asia Pacific Wireless Communications Symposium, APWCS 2023 - Tainan City, Taiwan
Duration: 23 Aug 202325 Aug 2023

Publication series

NameProceedings - 2023 VTS Asia Pacific Wireless Communications Symposium, APWCS 2023

Conference

Conference2023 VTS Asia Pacific Wireless Communications Symposium, APWCS 2023
Country/TerritoryTaiwan
CityTainan City
Period23/08/2325/08/23

Keywords

  • Artificial Intelligence
  • Deep Learning
  • Weaning from Mechanical Ventilator

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