New machine learning models developed at the University of Surrey in collaboration with University of California San Francisco (UCSF) are able to accurately predict the severity of three common symptoms faced by cancer patients (depression, anxiety and sleep disturbance), throughout the course of a patient’s treatment.
For the development, published in PLOS One, the researchers compared the results given by their machine and existing data on symptoms experienced by cancer patients during the course of computed tomography x-ray treatment. The predicted symptoms resulted very similar to the actual reported symptoms.
“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,” said Payam Barnaghi, Professor of Machine Intelligence at the University of Surrey.
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.”
Source: Science Daily