Publications by authors named "E Pfaff"
Purpose: TRK fusions are detected in less than 2% of central nervous system tumors. There are limited data on the clinical course of affected patients.
Experimental Design: We conducted an international retrospective cohort study of patients with TRK fusion-driven CNS tumors.
Introduction: To support long COVID research in National COVID Cohort Collaborative (N3C), the N3C Phenotype and Data Acquisition team created data designs to aid contributing sites in enhancing their data. Enhancements include: long COVID specialty clinic indicator; Admission, Discharge, and Transfer (ADT) transactions; patient-level social determinants of health; and in-hospital use of oxygen supplementation.
Methods: For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way.
Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection.
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- Patients recovering from COVID-19 often experience lingering symptoms known as Long COVID, which can manifest weeks or months after their initial infection, but the prevalence of this condition is not well understood.
- To address this, a collaborative initiative called the Long COVID Computational Challenge (L3C) was launched to develop effective risk prediction tools for identifying individuals at risk of Long COVID using extensive healthcare data from over 75 institutions in the U.S.
- The challenge resulted in 74 teams creating 35 predictive models, with the top models achieving high accuracy scores, demonstrating the potential for machine learning to enhance the identification of patients at risk for Long COVID.
Background: A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC).
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