Researchers analysed existing data of the symptoms experienced by cancer patients during the course of computed tomography x-ray treatment. The team used different time periods during this data to test whether the machine learning algorithms are able to accurately predict when and if symptoms surfaced. The results found that the actual reported symptoms were very close to those predicted by the machine learning methods.
Payam Barnaghi, Professor of Machine Intelligence at the University of Surrey, said: “These exciting results show that there is an opportunity for machine learning techniques to make a real difference in the lives of people living with cancer. They can help clinicians identify high-risk patients, help and support their symptom experience and pre-emptively plan a way to manage those symptoms and improve quality of life.”
Nikos Papachristou, who worked on designing the machine learning algorithms for this project, said: “I am very excited to see how machine learning and AI can be used to create solutions that have a positive impact on the quality of life and well-being of patients.”
Professor Adrian Hilton, Director of Surrey’s Centre for Vision, Speech and Signal Processing (CVSSP), said: “These exciting developments by Professor Barnaghi and his team show the incredible potential of machine learning in transforming the way healthcare professionals treat people suffering from cancer. We continue to explore the boundless potential of AI at CVSSP and we believe our work has a real place in helping to shape the future of health services across the globe.”
This work has been a collaboration between the University of Surrey and the University of California in San Francisco (UCSF). The UCSF research in this joint collaboration is led by Professor Christine Miaskowski.