Researchers from Princeton University are using machine learning to design a system that could reduce the frequency of tests and improve the timing of critical treatments for ICU patients. To create the system, the researchers used data from more than 6,060 patients admitted to the ICU between 2001 and 2012. The research team presented its results Jan. 6 at the Pacific Symposium on Biocomputing in Hawaii.

The analysis looked at four blood tests measuring lactate, creatinine, blood urea nitrogen and white blood cells. These indicators help diagnose two serious problems for ICU patients: kidney failure or sepsis.

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