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#News(IoTStack) [ via IoTGroup ]
Out of this came Tempo a technology for creating risk-based screening guidelines.Using an AI-based risk model that looks at who was screened and when they got diagnosed Tempo will recommend a patient return for a mammogram at a specific time point in the future like six months or three years.
Using the team’s previously developed risk-assessment algorithm Mirai Tempo obtained better early detection than annual screening while requiring 25 percent fewer mammograms overall at Karolinska.“By tailoring the screening to the patient’s individual risk we can improve patient outcomes reduce overtreatment and eliminate health disparities ” says Adam Yala a PhD student in electrical engineering and computer science MIT CSAIL affiliate and lead researcher on a paper describing Tempo published Jan. 13 in Nature Medicine.The development of AI-based risk models that operate over raw patient data give us an opportunity to transform screening giving more frequent screens to those who need it and sparing the rest ” says Yala.
“A key aspect of these models is that their predictions can evolve over time as a patient’s raw data changes suggesting that screening policies need to be attuned to changes in risk and be optimized over long periods of patient data.” The team needed the risk assessment at intermediate points so they designed their algorithm to learn a patient’s risk at unobserved time points from their observed screenings which evolved as new mammograms of the patient became available.This model then estimates patient risk at unobserved time points and it enables simulation of the risk-based screening policies.The model is dynamically changing the patient’s screening frequency based on how the risk profile is changing.Also the screening-cost metric which counts the total screening volume recommended by Tempo doesn’t provide a full analysis of the entire future cost because it does not explicitly quantify false positive risks or additional screening harms.Tempo could
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