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Department of Biomedical Informatics News and Stories


Research    Faculty    Artificial Intelligence (AI)

Yanjun Gao, PhD, Joins DBMI Faculty to Develop Clinical AI Tools

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.

Author Kara Mason | Publish Date June 25, 2024
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Research    Faculty

Harriet Dashnow, PhD, Joins DBMI Faculty to Research Rare Disease

This summer, Harriet Dashnow, PhD, will join the Department of Biomedical Informatics (DBMI) at the University of Colorado School of Medicine. In her new role as assistant professor, she’ll focus on computational method research, rare disease, and establish her own lab. 

Author Kara Mason | Publish Date January 09, 2024
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Research    Innovation    Faculty    Medical imaging

Novel Technology Designed to Increase MRI Speeds

Nicholas Dwork, PhD, assistant professor in the Department of Biomedical Informatics at the University of Colorado School of Medicine, has filed a provisional patent for a technology that could increase scan speeds of three-dimensional magnetic resonance imaging (MRI). The invention could lead to faster results, increase the clinical applications of MRIs, and ultimately improve patient care.

Author Toni Lapp | Publish Date December 05, 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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Research    COVID-19    Faculty    Public Health

How a COVID-19 Mortality Prediction Model Created by CU Data Scientists Could Provide Insights for the Next Pandemic

Overflowing intensive care units. A shortage of personal protective equipment. A scramble for hospital beds and ventilators. Health care workers pushed to the brink. The COVID-19 pandemic laid bare many well-documented vulnerabilities of health care systems. The need for accurate and early clinical assessment of severity related to COVID-19 was vital to developing crisis standards of care to meet the growing pandemic. These standards of care are informed by mortality prediction models, which assess the risk of imminent death in patients.

Author Toni Lapp | Publish Date September 22, 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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