Researchers at The University of Queensland are using artificial intelligence and machine learning (ML) to transform the vast streams of data generated in paediatric intensive care units into real-time clinical insights. By identifying subtle patterns that may signal deterioration before they become clinically apparent, the project aims to support faster decision-making, improve outcomes for critically ill children and advance the future of AI-enabled healthcare.
What's the problem?
Every child admitted to a paediatric intensive care unit generates an enormous volume of physiological and clinical information. Heart rate, blood pressure, oxygen saturation, laboratory results, ventilation parameters, medications and clinical observations create a continuous flow of data that changes from minute to minute.
For clinicians, recognising when a child's condition is beginning to deteriorate can be challenging, particularly when the earliest warning signs may be subtle and distributed across multiple data sources. The ability to identify deterioration earlier can be critical, enabling more timely interventions and potentially improving outcomes for some of the most vulnerable patients in the healthcare system.
What's UQ's innovation?
The UQ team is developing artificial intelligence and machine learning models capable of continuously analysing high-frequency clinical data generated in paediatric intensive care. Rather than relying solely on individual measurements or traditional monitoring approaches, the technology seeks to detect complex patterns and relationships across multiple data streams in real time.
By combining expertise in paediatric critical care, biostatistics, and data science, researchers are creating tools that can support clinicians with earlier warnings of deterioration and actionable insights at the point of care. The goal is not to replace clinical judgement, but to augment it by helping healthcare teams interpret large volumes of information more effectively and respond sooner when a child may be at risk.
The project is being delivered through UQ's Children's Intensive Care Research Program (ChIRP), a multidisciplinary research group dedicated to improving outcomes for critically ill children through innovation, data science and collaborative clinical research.
What's the impact?
This research demonstrates the potential of artificial intelligence to address one of the most significant challenges in critical care: turning large volumes of complex clinical data into meaningful and timely information.
If successful, the technology could support earlier identification of patient deterioration, faster and more informed clinical decision-making, improved outcomes for critically ill children and more personalised approaches to treatment. It also provides a pathway for integrating AI safely and effectively into clinical workflows, helping clinicians make better use of the information already available within healthcare systems.
More broadly, the project contributes to Australia's growing leadership in digital health, precision medicine and AI-enabled healthcare delivery. By demonstrating how advanced analytics can be applied in one of the most complex healthcare environments, the work has the potential to inform the future design of intelligent clinical decision-support systems across the health sector.
Did you know?
Professor Gibbons' Children's Intensive Care Research Program brings together clinicians, data scientists, nurses, bioinformaticians and researchers from multiple disciplines to improve outcomes for critically ill children. By combining artificial intelligence with real-world clinical expertise, the team is helping create a future where healthcare data can be used more effectively to support patient care when every moment matters.
Led by
- Professor Kristen Gibbons
Professorial Research Fellow
Group Lead, Children's Intensive Care Research Program (ChIRP)
Child Health Research Centre, The University of Queensland
UQ research team
The project brings together a multidisciplinary team with expertise spanning paediatric intensive care, data science, biostatistics and clinical research.
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| Industry Portfolio | Health and Biomedical |
Published 15 September 2026