The National Institutes of Health awarded the Laboratory of Applied Pharmacokinetics and Bioinformatics at the Children's Hospital Los Angeles $2.8 million over four years to use artificial intelligence to anticipate dosing and target the condition of individual critically ill patients over time and improve clinical treatment.

The lab will build a series of neural networks to predict variability in kidney function in children over time and how that influences their response to medication.

By tapping into the hospital's massive Virtual Pediatric Intensive Care Unit (VPICU) database, machine learning could unlock the patterns in the clinical measurements from 20,000 critically ill children who have been treated at the hospital since 2009, according to the announcement.  

Variables like medication volume and clearance in a child's body can change from day to day or moment to moment.

"Doctors can estimate the dose of medication needed, but that may not necessarily be the right dose for a particular patient. … We make models of drug systems in patients to try to understand how the drug is behaving," Dr. Michael Neely, professor of pediatrics and clinical scholar at the Keck School of Medicine of the University of Southern California, said in a statement.

Computer modeling of how medications behave in patients can account for dosing differences among individuals to some extent but is limited at using present or past measurements to predict future dosages. 

The researchers will test these algorithms using 5,000 VPICU blood plasma measurements of the antibiotic vancomycin to measure patient exposure over time.

"We are trying to anticipate what an unstable, critically ill child's kidney function will look like tomorrow, based on what's happened today, so we can predict that child’s medication needs," Neely said. 

Andrea Fox is senior editor of Healthcare IT News.
Email: afox@himss.org
Healthcare IT News is a HIMSS publication.