Research Faculty Artificial Intelligence (AI)
Computer scientist, researcher, and engineer Yanjun Gao, PhD, joins the Department of Biomedical Informatics at the University of Colorado School of Medicine as an assistant professor this fall, where she will focus on developing foundational natural language processing (NLP) technologies and conducting research on innovative artificial intelligence (AI) tools in clinical settings.
Research Clinical Informatics Artificial Intelligence (AI)
As the use of artificial intelligence (AI) increases in clinical spaces, so does research that aims to understand use, accuracy, and challenges. While most of this research has focused on clinical decision support or areas of specialty, few investigations have honed in on nursing.
Research Education Awards Artificial Intelligence (AI)
Faculty members from the University of Colorado Department of Biomedical Informatics and Department of Medicine have received a $1.8 million, five-year grant from the National Heart, Lung, and Blood Institute (NHLBI) to offer summer institutes focused on the ethical use of artificial intelligence (AI) and machine learning in data sciences and omics of cardiovascular and lung diseases.
Research Mental Health Artificial Intelligence (AI)
The human brain contains a lot of data.
“You often hear people say you only use 5 to 10% of the brain, but it’s not true,” says Joel Stoddard, MD, MAS, associate professor of psychiatry and secondary faculty member in the Department of Biomedical Informatics at the University of Colorado School of Medicine.
Research Artificial Intelligence (AI)
As the world explores the new possibilities and uses of artificial intelligence (AI) tools like ChatGPT, researchers at the University of Colorado Department of Biomedical Informatics (DBMI) are integrating similar models into academic authoring.
IEEE Xplore
July 10, 2024
In this manuscript we explore a computationally efficient approximation to hard data consistency. We present results when adding this data consistency layer into two existing networks designed for MRI reconstruction. After retraining with the additional consistency layer, the networks show improved out-of-distribution performance and suppression of hallucinations.
JAMIA
July 10, 2024
The publication of the Phoenix criteria for pediatric sepsis and septic shock initiates a new era in clinical care and research of pediatric sepsis. The phoenix R package and Python module enable researchers to apply the Phoenix criteria to electronic health records (EHR) datasets and derive the relevant indicators, total scores, and sub-scores.
JAMIA
July 10, 2024
The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven’s Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline.
SPIE
July 03, 2024
Nick Dwork, PhD, and co-authors present a method that combines compressed sensing with parallel imaging that takes advantage of the structure of the sparsifying transformation.
Kara Mason
