Machine learning shows promise in optimizing ICU blood tests

A computational approach has the potential to help clinicians in intensive care units make better decisions about ordering common blood tests. Results of their study, presented earlier this month at the 2019 Pacific Symposium on Biocomputing, showed that using a machine learning algorithm developed by Princeton University researchers could have reduced the number of lab orders for white blood cell tests by as much as 44 percent. In addition, researchers demonstrated that their approach would have helped inform clinicians to intervene sometimes hours sooner when a patient's condition began to deteriorate. "With the lab test ordering policy that this method developed, we were able to order labs to determine that the patient's health had degraded enough to need treatment, on average, four hours before the clinician actually initiated treatment based on clinician ordered labs," says Barbara Engelhardt, senior author of the study and associate professor of computer science at Princeton. In their study, researchers leveraged the MIMIC III database--which includes detailed medical records of 58,000 critical care admissions at Boston's Beth Israel Deaconess Medical Center--and selected a subset of 6,060 records of adults who were admitted to the ICU between 2001 and 2012.