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

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posted on 18.02.2021, 09:51 authored 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.


Campus location


Principal supervisor

Chiew Yeong Shiong

Additional supervisor 1

Tan Chee Pin

Year of Award


Department, School or Centre

School of Engineering (Monash University Malaysia)


Doctor of Philosophy

Degree Type