An advanced AI developed at the University of Surrey could give doctors a head start for treating cancer. The latest AI is capable of predicting symptoms and their level of severity during the course of a patient’s treatment.
The study, reported in the PLOS One journal, is believed to be the first of its kind. Scientists from the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey described how they developed a pair of machine-learning models that are capable of exactly predicting the severity of three usual symptoms—that is, anxiety, depression, and sleep disturbance—faced by cancer patients. These three common symptoms can considerably reduce the quality of life in cancer patients.
Scientists examined the prevalent data of the symptoms that cancer patients experienced throughout the course of computed tomography X-ray treatment. During the analysis of this data, the researchers used different periods of time to find out whether the machine-learning algorithms have the potential to precisely predict when and if symptoms appeared.
The results revealed that the real reported symptoms were quite close to those forecasted by the machine learning techniques.
The research has been a joint effort between the University of California in San Francisco (UCSF) and the University of Surrey. Professor Christine Miaskowski headed the UCSF research in this joint collaboration.
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.
Payam Barnaghi, Professor, Machine Intelligence, University of Surrey.
Nikos Papachristou, who worked on creating the machine-learning algorithms for this project, stated, “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.”