Machine Learning Can Make Lab Testing More Precise

An analysis of over 2 billion lab test results suggests a deep learning model can help create personalized reference ranges, which in turn would enable clinicians to monitor health and disease better.Paul Cerrato, MA, senior research analyst and communications specialist, Mayo Clinic Platform and John Halamka, M.D., president, Mayo Clinic Platform, wrote this article.Almost every patient has blood drawn to measure a variety of metabolic markers. Typically, test results come back as a numeric or text value accompanied by a reference range which represents normal values. If total serum cholesterol level is below 200 mg/dl or serum thyroid hormone level is 4.5 to 12.0 mcg/dl, clinicians and patients assume all is well. But suppose Helen ’s safe zone varies significantly from Mary’s safe zone. If that were the case, it would suggest a one-size-fits-all reference range misrepresents an individual’s health status. That position is supported by studies that found the distribution of more than half of all lab test results, which r ely on standard reference ranges, differ when personal characteristics are considered.1With these concerns in mind, Israeli investigators from the Weismann Institute and Tel Aviv Sourasky Medical Center extracted data on 2.1 billion lab measurements from EHR records, taken from 2.8 million adults for 92 different lab tests. Their goal was to create “data-driven reference ranges that consider age, sex, ethnicity, disease status, and other relevant ch...
Source: Life as a Healthcare CIO - Category: Information Technology Source Type: blogs