Asco Daily News

Drs. Shaalan Beg and Travis Osterman discuss a machine learning model, recently featured in JCO Clinical Cancer Informatics, that uses electronic health record data to accurately predict the effectiveness and toxicity of treatment with immune checkpoint inhibitors. The new AI model can be used to provide a personalized risk-benefit profile, inform therapeutic decision-making, and improve clinical trial cohort selection.   TRANSCRIPT Dr. Shaalan Beg: Hello, and welcome to the ASCO Daily News Podcast. I'm Dr. Shaalan Beg, your guest host for today. I am an adjunct associate professor at UT Southwestern's Simmons Comprehensive Cancer Center.  Cancer immunotherapy has transformed the treatment landscape by providing new and effective treatment options for many solid and hematologic malignancies. But while many patients experience a remarkable response to immune checkpoint inhibitors, other patients can suffer life-threatening immune checkpoint toxicities. Today, we will be discussing a machine learning solution