MACHINE LEARNING & AI: A HIMSS EVENT
LAS VEGAS, NV - MARCH 5, 2018
This session will discuss the results of a study that used machine learning to analyze EMR data to identify individuals at high risk of colorectal cancer (CRC).
The study included 9,108 controls and 900 cancer cases among Kaiser Permanente Northwest adults, ages 40-89. The model identified individuals with a tenfold higher risk of undiagnosed colorectal cancer at curable stages, and flagged colorectal tumors 180-360 days prior to usual clinical diagnosis.
The detection model can be applied to broad populations to identify people at increased risk of CRC (in particular, right-sided CRC), and enables health systems to more effectively target colonoscopy resources.
This study also demonstrated the feasibility for the model’s use in a U.S.-based HMO adult population.
This CRC detection model narrows the screening gaps associated with people who decline fecal tests and/or colonoscopies, and instead opportunistically analyzes existing demographic data and CBC tests.
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