Machine learning can shrink 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, the researchers designed a system that could both reduce the frequency of tests and improve the timing of critical treatments.

The analysis focused on four blood tests measuring lactate, creatinine, blood urea nitrogen and white blood cells. These indicators are used to diagnose two dangerous problems for ICU patients: kidney failure or a systemic infection called sepsis.


The researchers worked with the MIMIC III database, which includes detailed records of 58,000 critical care admissions at Beth Israel Deaconess Medical Center in Boston. For the study, the researchers selected a subset of 6,060 records of adults who stayed in the ICU for between one and 20 days and had measurements for common vital signs and lab tests.


The team's algorithm uses a "reward function" that encourages a test order based on how informative the test is at a given time. That is, there is greater reward in administering a test if there is a higher probability that a patient's state is significantly different from the last measurement, and if the test result is likely to suggest a clinical intervention such as initiating antibiotics or assisting breathing through mechanical ventilation. At the same time, the function adds a penalty for the test's monetary cost and risk to the patient.


Depending on the situation, a clinician could decide to prioritize one of these components over others. This approach, known as reinforcement learning, aims to recommend decisions that maximize the reward function.

Overall, the researchers' analysis showed that their optimized policy would have yielded more information than did the actual testing regimen that clinicians followed. Using the algorithm could have reduced the number of lab test orders by as much as 44 percent in the case of white blood cell tests. They also showed that this approach would have helped inform clinicians to intervene sometimes hours sooner when a patient's condition began to deteriorate.


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