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Department of Biomedical Informatics News and Stories


Research    Education    Community

CU Department of Biomedical Informatics Top Stories of 2023

This year marked the one-year anniversary of the Department of Biomedical Informatics (DBMI) at the University of Colorado School of Medicine and along with that celebration came many more, ranging from crucial research to exciting new grant announcements.

As 2023 comes to an end, join the communications staff in revisiting some of the most important stories from the year and looking to what’s next for DBMI faculty and researchers.

Author Kara Mason | Publish Date December 15, 2023
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Research    Community    COVID-19

How CU Data Scientists Are Debunking COVID-19 Misinformation

Researchers in the Department of Biomedical Informatics (DBMI) at the University of Colorado School of Medicine spearheaded an innovative method to synthesize and compile accurate information about COVID-19 since the onset of the global pandemic in 2020.

Author Toni Lapp | Publish Date January 12, 2023
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Research    Community    Equity Diversity and Inclusion

CU Professor Draws on Indigenous Roots in Approach to Research and Mentoring

From the University of Colorado Department of Biomedical Informatics (DBMI) offices on the top floor of the Anschutz Health Sciences Building, one sees sweeping views of Denver and the Rocky Mountains. DBMI Assistant Professor Katrina Claw, PhD, sees the lands that Native American tribes have called their home.

Author Toni Lapp | Publish Date November 21, 2022
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Community    Faculty    Diversity    Health equity    Social Justice

Breaking Barriers and Creating Opportunities for Underrepresented People in Data Science

Janani Ravi, PhD, assistant professor in the Department of Biomedical Informatics at the University of Colorado School of Medicine, didn’t intend to challenge the status quo when she began her career in data science. But after several years of working diligently on her research in computational biology as a graduate student at Virginia Tech, as a postdoc at Rutgers Biomedical and Health Sciences, and then as an independent researcher/faculty member at Michigan State University (MSU), she began to see a pattern emerge, particularly on the conference circuit.

Author Toni Lapp | Publish Date November 11, 2022
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Department of Biomedical Informatics In the News

IEEE Xplore

Deep Learning with Enforced Data Consistency

news outletIEEE Xplore
Publish DateJuly 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.

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phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and criteria

news outletJAMIA
Publish DateJuly 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.

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MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance

news outletJAMIA
Publish DateJuly 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.

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Accelerated parallel magnetic resonance imaging with compressed sensing using structured sparsity

news outletSPIE
Publish DateJuly 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.

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