Machine learning could reduce testing, improve treatment for intensive care patients

Doctors in intensive care units face a continual dilemma: Every blood test they order could yield critical information, but also adds costs and risks for patients. To address this challenge, researchers from Princeton University are developing a computational approach to help clinicians more effectively monitor patients' conditions and make decisions about the best opportunities to order lab tests for specific patients. Using data from more than 6,000 patients, graduate students Li-Fang Cheng and Niranjani Prasad worked with Associate Professor of Computer Science Barbara Engelhardt to design a system that could both reduce the frequency of tests and improve the timing of critical treatments. The team presented their results on Jan. 6 at the Pacific Symposium on Biocomputing in Hawaii. The analysis focused on four blood tests measuring lactate, creatinine, blood urea nitrogen and white blood cells.