Children’s National Health System in Washington, D.C., is collaborating with technology company KenSci to study the use of artificial intelligence to improve pediatric risk scores in the intensive care unit.
Through the research collaborative, Children's National and KenSci, a company that develops machine learning software, will work together to develop new models to understand the factors that impact criticality in pediatric patients. The organizations aim to enhance pediatric risk scores to improve clinical decision-making in critical care.
Risk scores have been used since the mid-'80s to predict mortality risks in pediatric ICUs, according to Murray Pollack, M.D., director of outcomes research at Children’s National and a professor of pediatrics at George Washington University School of Medicine. In most cases, these scores are used for quality assessment, he said.
“Our collaborative goals are to study the temporal variation in data, taking the first step towards dynamic risk scoring for pediatric ICUs," Pollack said.
Hiroki Morizono, Ph.D., director of biomedical informatics at the Children’s National Center for Genetic Medicine Research, said the AI modeling could predict an individual patient’s likelihood of deterioration or improvement.
“We see tremendous possibilities for how this research can be used safely and securely to supplement the clinician’s judgment,” Morizono said.
The joint team will use KenSci's AI platform to test different models and compare their predictive effectiveness to prior baselines developed by Children’s National and George Washington University.
Ankur Teredesai, KenSci’s co-founder and chief technology officer, said in a statement: “Time is our best ally if integrated appropriately with other variables in healthcare machine learning and AI. Adding dynamism holds tremendous promise to be assistive for critical care."
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