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Found 6 papers, 2 papers with code
CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines
Synthetic Electronic Health Records (EHR) have emerged as a pivotal tool in advancing healthcare applications and machine learning models, particularly for researchers without direct access to healthcare data.
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Translational research requires data at multiple scales of biological organization.
Causal Fairness Assessment of Treatment Allocation with Electronic Health Records
Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice.
Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality
Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping.
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records
To adjust for unobserved confounders, we develop the medical deconfounder, a machine learning algorithm that unbiasedly estimates treatment effects from EHRs.
Characterizing Design Patterns of EHR-Driven Phenotype Extraction Algorithms
Good classification accuracy with simple features demonstrated the attribution coherence and the feasibility of automatic identification of design patterns.
