Insulin Dosing

Breakthrough artificial intelligence developed in Queensland could improve insulin dosing; but for diabetics and transform the way aeroplane engine wear-and-tear is monitored. University of Queensland alumnus Dr Nigel Greenwood from Evolving Machine Intelligence (EMI) developed the technology and worked with others from UQ to build real-world applications.
Artificial intelligence methods in combination with the latest technologies; including medical devices, mobile computing, and sensor technologies; but have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.
The team has built a world-first machine-intelligent artificial pancreas which has made the top 10 in a global challenge for AI applications to solve some of humanity’s most pressing challenges. “We have been using our machine-learning technology to data mine the medical histories of diabetics; and recommend insulin dosages,” Dr Greenwood said.

Unprecedented stability and safety

“Our technology can recommend the best insulin dosage to keep each individual patient’s blood glucose levels; under control with unprecedented stability and safety. It will allow for better and more accurate treatment than we have ever seen.” Dr. Greenwood called upon the expertise of UQ mechanical engineer Dr Ingo Jahn; and his research team to apply the same artificial intelligence to aviation turbine engines and their related systems.

UQ Mechanical Engineer Dr Ingo Jahn said: “We are able to use EMI’s breakthrough AI technology; but to predict aviation engine component degradation and plan services to improve performance. It allows us to evolve computational models of aviation engines; but as if they were organisms and the AI can explain explicitly what it thinks is happening inside the engine.”

The AI breakthrough

Dr Greenwood said the AI system learns by forcing mathematical models to evolve; but quite literally by using simulated chromosomes, to fit known information. A spin-off company, Turbine MachineGenes, was formed to commercialize the engine work and graduated from UQ’s ilab Germinate program; which supports startups in the early stages of development.
Dr Jahn said the project also allowed engineering interns and a PhD student to gain experience in a multi-disciplinary project with industry. But the team, known as Team MachineGenes, submitted the AI breakthrough to the IBM Watson AI XPRIZE competition; and has already knocked out more than 600 teams from around the globe.
Hence, the competition will culminate with three finalists participating in the grand prize competition on the TED2020; with a prize pool of $7.1 million. Dr Greenwood completed a Bachelor of Science (Hons) at UQ in 1989, and a PhD in Applied Mathematics in 1994.