Project
Expanding Data Sets to Allow Improved Critical Care for Children – Inpatient Risk Prediction
In collaboration with Queensland Health, this project aims to provide a platform-independent decision support framework using an interpretable machine learning approach for making effective risk predictions for paediatric patients at risk of sepsis. These predictions will be based on multi-source, readily available, critical care data. They are intended to be a practical electronic clinician assistant and provide new insights into clinical decision-making.
About the Project
In collaboration with Queensland Health, this project aims to provide a platform-independent decision support framework using an interpretable machine learning approach for making effective risk predictions for paediatric patients at risk of sepsis. These predictions will be based on multi-source, readily available, critical care data. They are intended to be a practical electronic clinician assistant and provide new insights into clinical decision-making.
The challenges of the project include effective linkage of relevant data together from different medical data sources, computational analysis of the fused representations in a real-time manner, medical interpretability formulation, uncertainty regarding the reference standard diagnosis of sepsis and integration of interpretability and accurate prediction in a joint training scheme. The significance of this project centres on the novel linkage-analysis and usage of novel transparent algorithmic development to open the black-box ML algorithms in the medical domain.
The research aims to showcase medical analysis in a data-rich environment, with the proposed model and research tasks adapted and applied to other medical predictive tasks, for instance, sepsis prediction/monitor, linking genomics with risk prediction, and so on.
This project commenced in April 2023 with the recruitment of PhD researcher “Huy” Van Nhat Huy Nguyen who is based at The University of Queensland (UQ). Huy will collaborate closely with leading experts in Queensland Health on investigating algorithm use and organizational implications in inpatient settings. He is supervised by Dr Sen Wang and Dr Ruihong Qiu from UQ, and Associate Professor Kristen Gibbons and Dr Sainath Raman from Queensland Health. The project will develop a platform-independent decision support framework using an interpretable machine learning approach to make effective risk predictions for pediatric patients at risk of sepsis.
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 – Outpatient 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
Huy Nguyen
PhD Researcher
Dr Sen Wang
Chief Investigator
A.Prof Kristen Gibbons
Partner Investigator
Dr Sainath Raman
Associate Supervisor
Dr Ruihong Qiu
Associate Supervisor
Partner
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