Data Analytics team

Physiological Modelling

Physiological models are mathematical models characterized by a physiologically consistent mathematical structure (defined by the set of equations being used) and a set of model parameters to be estimated in the most precise and accurate way. In our research group, we have developed physiological models to better describe human systems physiology. This allows us to better understand patient’s condition, providing opportunities to guide medical treatment.

Figure: Sample of respiratory system ventilation models.

Application of Model-based Methods and Artificial Intelligence for Medical Treatment 

Model-based methods and application of artificial intelligence potentially offer non-invasive and cost-effective treatment in hospital care. In particular, mathematical or machine learning models of the patient's physiological system can be developed to describe the patient's condition using retrospective clinical data. For example, retrospective data of mechanically ventilated respiratory failure patient can be used to develop a virtual patient for investigation of the effectiveness of a model-based treatment. The virtual patient l potentially reduces the need for multiple clinical trials that are exhaustive for testing the performance of medical treatment.

Figure: Schematics of how  application of Model-based methods and AI can be used to improve mechanical ventilation treatment in the hospital ICU.

Instrumentations and Data Analytical Software developments

Instrumentation is the development in technology and use of precise measuring equipment to provide precise measuring and analytical tools for studying, monitoring, or controlling a specific engineering application. Together with data analytical software, these instrumentations provide a means to process and transform data into useful information in supporting decision-making processes.

Figure: An automated data acquisition, monitoring and decision support system developed for hospital intensive care mechanical ventilation treatment

Dr Yeong Shiong Chiew

Dr Chiew heads the Data Analytics team in the Biomedical Engineering and Simulation Group at Monash University Malaysia. He graduated from Universiti Teknologi Malaysia, with a BEng Mechanical-Automotive and a MEng. He obtained his PhD at the University of Canterbury investigating into model-based mechanical ventilation for patients with respiratory failure. He is currently a Senior Lecturer in Monash University Malaysia, pioneering model-based clinical mechanical ventilation trial in hospitals. His work spans from the development of physiological models, model development, algorithm and system identification methods, and the creation of unique clinical decision metrics, to conducting clinical and animal trials.

Dr Chiew is a member of the IFAC TC 8.2. Biological and Medical System and a Chartered Engineer of the IMechE UK. He serves as an Editorial board member in the BMC Pulmonary Medicine Journal and in the BioMed Research International journal.

Current GRS

Mr Chin Hooi Ng

Chin Hooi received his B.Eng (Hons) in Mechatronics Engineering from Monash University Malaysia in 2019. He worked as a Research Assistant in Monash, researching in the field of Computer Vision before pursuing his Master’s Degree focusing on object detection and tracking in surveillance systems for pedestrians and vehicles.

Mr Christopher Yew Shuen Ang

Christopher graduated with a Bachelor's Degree (First Class Honours) in Mechanical Engineering from Monash University in May 2022. He is listed in the Faculty of Engineering Dean's Honours List and was also the overall best graduate of his graduation class. He was involved in biomedical engineering research during his undergraduate studies, Final Year Project and subsequently as a research assistant, researching in the field of mechanical ventilation. Christopher joins the BEMS group as a PhD student in 2022, where he investigates the feasibility of stochastic integrated virtual patient modelling for mechanical ventilation treatment.

Alumni

Dr Ganesaramachandran Arunachalam

Dr Ganesa Ramachandran graduated with Bachelor of Technology from Madras University, India and Master of Science in the field of Biomedical Engineering from Aachen University, Germany. He obtained his PhD degree from Monash University Malaysia in 2022 where his research focuses on modelling spontaneous breathing dynamics of respiratory failure patients.

Mr Jay Wing Wai Lee

Jay graduated with his Bachelor of Mechanical Engineering from Monash University Malaysia in 2019 and subsequently began his Masters in Engineering Research in Monash University Malaysia the following year. He completed his Master degree in May 2022 with his work on using stochastic modelling to develop virtual patients with the aim to optimise mechanical ventilator settings.

Mr Qing Arn Ng

Qing Arn received his B.Eng (Hons) in Mechanical Engineering from Monash University Malaysia in 2019. He obtains his Master degree in May 2022, with his research focusing on developing a network-based data acquisition system for intensive care mechanical ventilator system.

Dr Nien Loong Loo

Nien Loong continued his studies as a doctoral student at Monash University Malaysia in 2017 where his research focus is mainly on the field of biomedical engineering. He explored the field of utilising artificial intelligence to improve mechanical ventilation treatment for patients with respiratory failure.  Warren successfully completed his PhD studies in February 2020 where he has contributed to understanding the severity of ventilation asynchrony using an autoencoder machine learning model.

Mr See Loong Li

See Loong pursued his postgraduate studies with Monash University Malaysia in 2018 as a MEngSc student. His research focuses on investigating the trends of haemodialysis of deceased patients and feasibility of model-based methods for haemodialysis treatment. In 2021, See Loong successfully completed his Master studies, opening up opportunities in new research area in model-based haemodialysis treatment in Malaysia.

• Mr Shaphan Zhu Ming Lim (2017-2018)
• Mr Vimal Mothi (2018)
• Mr Yi Jie Tan (2019)
• Mr Daniel Xien Sim (2019-2020)
• Mr Qing Arn Ng (2019-2020)
• Mr Chin Hooi Ng (2020)
• Mr Charles Wen Xuan Ting (2020)
• Mr Sulaiman Shah Azlan Shah (2020-2021)