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Department of Biomedical Informatics News and Stories

rare disease

Research    rare disease

CU Data Scientists Develop Rare Disease Phenopacket Standard, Tools For Global Use

Researchers in the Department of Biomedical Informatics (DBMI) at the University of Colorado School of Medicine have reached a major milestone in developing standards and tools for creating phenopackets that may foster more innovation and advancement in the medical field by allowing health professionals to more easily collect and share data.


Author Kara Mason | Publish Date May 17, 2023
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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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JAMIA

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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JAMIA

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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SPIE

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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