Experts have developed a machine learning model they say can help predict which patients are most likely to require some form of acute or urgent care while undergoing a radiation therapy treatment regimen. It is estimated that up to 20% of patients undergoing radiotherapy will require acute care in the form of emergency visits or hospitalizations at some point during their treatment.
We previously reported the results of the System for High-Intensity Evaluation During Radiotherapy (SHIELD-RT), one of the first machine learning (ML)-guided randomized controlled trials in healthcare, where ML was applied to electronic health record (EHR) data to identify patients at high risk for acute care events and direct increased clinical evaluations – reducing acute care by 45% and overall costs by 48%,
University of California San Francisco reacher team noted.
SHIELD-RT classified around 20% of the cases as high-risk. True event rates for the high- and low-risk populations were 13.9% and 2.8% at Site 1, 16.5% and 3.7% at Site 2, “demonstrating good discriminatory power,” the group suggested.
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