ARTIFICIAL INTELLIGENCE/MACHINE LEARNING ASSISTED NEWBORN CRITICAL CARE (VIRTUAL CRITICAL CARE MODELING)

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Summary

The most vulnerable critical care patients are newborn babies both those extremely premature and sick full term. High resolution physiological data is displayed for all patients but typically purged after 2 days in most units. Only single linear trends obvious to the naked eye are normally used by the intensive care team. Multi-parameter non-linear pattern recognition of this data using AI/ML will build the virtual intensive care unit of the future, today. Preclinical detection of life threatening and permanently brain damaging diseases that can cause cerebral palsy, blindness, deafness, intellectual impairment or autism disorders allows the real possibility of prevention to improve life long outcomes.

Supervisor(s)

Professor Alistair McEwan

Research Location

Biomedical Engineering

Program Type

PHD

Synopsis

The NICU at Westmead together with the University of Sydney School of biomedical Engineering has a team dedicated to the development the virtual newborn intensive care unit. Westmead NICU has a permanent data storage of all intensive care physiology over more than 7 years providing one of the richest datasets for this population anywhere in the world. We have projects in prediction of clinical outcomes, visualisation and creating a true digital twin or virtual baby of each neonate.

Additional Information

These projects require excellent programming and data analytics skills along with a strong interest in biomedical engineering and newborn care.

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Keywords

artificial intelligence, Machine learning, Critical Care

Opportunity ID

The opportunity ID for this research opportunity is: 2922

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