artificial intelligence (AI) Archive — Page 6 of 7
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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. -
February 13, 2020
Algorithm helps improve coronary calcium detection
A new algorithm for artificial intelligence-assisted calcium scoring can accurately determine cardiovascular risk across a range of CT scans and in a racially diverse population. -
January 15, 2020
VUMC study to use artificial intelligence to explore suicide risk
Investigators will use computational methods to shed light on suicidal ideation and its relationship to attempted suicide, predict suicidal ideation and suicide attempt using routine electronic health records (EHRs) and explore the genetic underpinnings of both.