artificial intelligence (AI) Archive — Page 6 of 7
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November 16, 2021
AI predicts 24-hour hospital discharge
Vanderbilt researchers used a machine learning algorithm and data from more than 26,000 hospital stays to predict who would and would not be discharged over the next 24 hours. -
October 19, 2021
Artificial intelligence predicts opioid overdose in Tennessee
Researchers at Vanderbilt and the Tennessee Department of Health have developed 30-day predictive models for fatal and non-fatal opioid-related overdose among patients receiving opioid prescriptions in the state. -
September 30, 2021
AI predicts next-day delirium or coma in ICU patients
A team at Vanderbilt University Medical Center used machine learning to predict the likelihood of next-day brain function status changes in critical care patients. -
January 5, 2021
VUMC, Case Western apply artificial intelligence to “customize” oral cancer treatment
Researchers at Vanderbilt University Medical Center and Case Western Reserve University in Cleveland have been awarded a five-year, $3.3 million grant by the National Cancer Institute to apply artificial intelligence (AI) to help customize treatment for oral cancer patients. -
December 10, 2020
Model students: improving clinical decision-making
Vanderbilt investigators have devised a system to alert health IT teams to deteriorating performance in clinical prediction models. -
September 17, 2020
Grant from Google to support COVID gene expression study
Vanderbilt University Medical Center and the University of North Carolina (UNC) at Chapel Hill have been awarded $500,000 by Google’s philanthropy, Google.org, to study how COVID-19 alters gene expression in some people in ways that may be linked to their risk of severe illness and death. -
August 27, 2020
Study uses AI to sort patient messages by complexity
Taking an interest in electronic message threads between surgical patients and their health care teams, a research group at Vanderbilt University Medical Center has tested how well certain commonly used machine learning algorithms can classify such exchanges according to their clinical decision-making complexity.