Machine Learning Models to Help Predicting Cancer Symptoms, Plan Treatment

In what could be regarded as a first of its kind, researchers from the Centre for Vision, Speech and Signal Processing at the University of Surrey in the United Kingdom have recently reported that they created two machine learning models that are able to precisely predict the severity of three common symptoms faced by cancer patients: depression, anxiety and sleep disturbance. And, these three symptoms play potential roles in diminishing the quality of life in cancer patients.

During the research, the researchers analyzed the data of symptoms experienced by cancer patients during the course of computed tomography X-ray treatment. The team used different time periods to test whether the machine learning algorithms are able to accurately predict the severity of symptoms and also if the symptoms surfaced.

The results showed that the actual reported symptoms were very close to those predicted by the machine learning algorithms. This work has been a collaboration between the University of Surrey and the University of California in San Francisco (UCSF).

In the context of the research, 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- actually plan a way to manage those symptoms and improve quality of life.”

In essence, doctors could get an edge in treating cancer by implementing the machine learning algorithms, which are able to predict symptoms and the corresponding severity throughout the course of the treatment of a cancer patient.