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Asynchronous Breathing and Patient Ventilator Interaction Assessment: A Machine Learning Approach

thesis
posted on 18.02.2021, 09:51 by NIEN LOONG LOO
While the prevalence and consequences of asynchronous breathing (AB) due to suboptimal patient-ventilator interaction (PVI) is unknown, a method to investigate the impact of AB on patient’s outcome is required. For this reason, this thesis investigates the use of machine learning algorithms in assessing the quality of PVI. These models can potentially aid clinicians to evaluate the patient’s condition during mechanical ventilation treatment; thus, allowing better decision making.

History

Campus location

Malaysia

Principal supervisor

Chiew Yeong Shiong

Additional supervisor 1

Tan Chee Pin

Year of Award

2021

Department, School or Centre

School of Engineering (Monash University Malaysia)

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Exports