When the opportunity to take part in summer research came up, Adam Molnar jumped at it. He studied Engineering Physics at Chalmers University of Technology for three years and is now in the later stages of the Medical Physicist Programme at the University of Gothenburg, which leads to both a Degree of Master of Science in Medical Physics and a Degree of Master of Medical Science with a major in Medical radiation science.
“I could have taken another summer job where I probably would have earned more. But this feels so much more meaningful. It’s exciting to be part of a project that could make a difference while also getting to see what research is like in practice,” says Adam, whose summer research is funded by the Axel Lennart Larsson Fund.
Adam spends much of his time programming. He is developing code that extracts and standardizes information from ECG files from different systems. It is detective work: each file format has to be understood before the data can be used for further analysis. Because the work takes place in a secure research environment and involves sensitive healthcare data, AI tools cannot be used for coding assistance, making the task even more challenging.
Large volumes of data
ECG is one of the most commonly performed tests in healthcare and generates vast amounts of data every day. Adam’s supervisor is Martin Adiels, Associate Professor of Health Science Statistics at Sahlgrenska Academy, University of Gothenburg.
“Internationally, this is a very hot area of research. We’ve been thinking about how to get started, and Adam is helping us figure out how to move forward. Once we have the data in place, we can start testing existing AI models on our own ECG data,” he says.
The project also gives Adam a chance to find out whether research is something he would like to pursue in the future. What appeals to him most is the combination of programming, machine learning, and medical data.
“There is so much healthcare data that could be used in new ways. Machine learning can identify patterns that would be difficult or impossible to detect manually. I find that really exciting.”
