Machine Learning Could Reduce Lab Testing for ICU Patients
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Princeton researchers have found that machine learning can be used to improve treatment and reduce lab testing for patients in intensive care units (ICUs).
The researchers used data from more than 6,000 ICU patients to design a machine learning-based system that could reduce frequency of lab tests and improve timing of critical treatments.
The researchers used the MIMIC III critical care database, which includes records of 58,000 critical care admissions at Beth Israel Deaconess Medical Center. For the study, they selected a subset of 6,060 records of adults who stayed in the ICU for up to 20 days and had measurements for common vital signs and lab tests.
“These medical data, at the scale we’re talking about, basically became available in the last year or two in a way that we can analyze them with machine learning methods,” said Princeton Associate Professor of Computer Science Barbara Engelhardt, who is the senior author of the study.
The analysis focused on four blood tests measuring lactate, creatinine, blood urea nitrogen, and white blood cells. These were used to diagnose two critical issues for ICU patients—kidney failure and sepsis. 【MORE】
