Blood tests are currently one-size-fits-all machine learning can pinpoint whats truly normal for each patient [Video]

If you’ve ever had a doctor order a blood test for you, chances are that they ran a complete blood count, or CBC. One of the most common blood tests in the world, CBC tests are run billions of times each year to diagnose conditions and monitor patients’ health.

But despite the test’s ubiquity, the way clinicians interpret and use it in the clinic is often less precise than ideal. Currently, blood test readings are based on one-size-fits-all reference intervals that don’t account for individual differences.

I am a mathematician at the University of Washington School of Medicine, and my team studies ways to use computational tools to improve clinical blood testing. To develop better ways to capture individual patient definitions of “normal” lab values, my colleagues and I in the Higgins Lab at Harvard Medical School examined 20 years of blood count tests from tens of thousands of patients from both the East and West coasts.

In our newly published …