Project
Expanding Data Sets to Allow Improved Critical Care for Children – Outpatient Risk Prediction
This project, in collaboration with Queensland Health, aims to develop a probabilistic cloud-based health monitoring and risk prediction system that can predict clinical abnormalities based on streaming data of vitals of children with possible serious infection at home.
About the Project
The traditional hospital-focused model of care neglects monitoring and treating diseases at home. A number of intelligent monitoring systems exist for clinical abnormalities prediction for patients who are confined to hospital beds, but few attempts have been made to develop such a predictive system for home care that could prevent and minimize health-related risk at an early stage. As a result, recovering patients who leave the hospital after treatments are known to have adverse outcomes due to lack of efficient alert systems. In particular, children at risk of serious infection and sepsis are often discharged home with ‘safety-netting’ advice for parents and carers to subjectively observe signs of deterioration.
This project, in collaboration with Queensland Health, aims to develop a probabilistic cloud-based health monitoring and risk prediction system that can predict clinical abnormalities based on streaming data of vitals of children with possible serious infection at home.
The project commenced in April 2023 with the recruitment of PhD researcher Hrishi Patel, who is based at The University of Queensland. Hrishi is supervised by Dr Sen Wang, Dr Adam Irwin (Queensland Health), and Professor Shazia Sadiq. This project is a close collaboration with leading experts in Queensland Health on investigating machine learning techniques for designing risk predictive models in outpatient settings, to mitigate the risk of sepsis in children.
This project is one of three CIRES projects with Queensland Health related to paediatric sepsis management. Further details on the other projects can be found via the project pages below:
- Improving Sepsis Management through Better Data and Rapid Learning
- Expanding Data Sets to Allow Improved Critical Care for Children – Inpatient Risk Prediction
Queensland Health wants to constantly improve the health of Queenslanders and the care they receive and aims to capture all relevant longitudinal data on Queenslanders to understand about their health – past, current and anticipated, treatment decisions made, and outcomes experienced. However, it is complicated to capture the right information once, curate it and access the information in an ethical and consented way that protects the individual privacy of Queenslanders. A partnership with CIRES bridges a major existing gap and represents the glue that can enable the data scientists, the clinicians and consumer community to help get this right.
Recognising that sepsis is a leading cause of preventable harm in children, Queensland Health clinicians launched the Queensland Paediatric Sepsis Program. There are three CIRES projects with Queensland Health which focus on how clinicians can best implement tools derived from transparent technical solutions to improve the recognition of sepsis in children. In one of these we will apply the power of data and machine learning algorithms to develop a decision tool that supports early detection and management of sepsis in children.
Project Team
Hrishi Patel
PhD Researcher
Dr Sen Wang
Chief Investigator
Prof. Shazia Sadiq FTSE
Centre Director
Dr Adam Irwin
Partner Investigator
Partner
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