Publications by authors named "Janos G Hajagos"
Opioid overdose (OD) has become a leading cause of accidental death in the United States, and overdose deaths reached a record high during the COVID-19 pandemic. Combating the opioid crisis requires targeting high-need populations by identifying individuals at risk of OD. While deep learning emerges as a powerful method for building predictive models using large scale electronic health records (EHR), it is challenged by the complex intrinsic relationships among EHR data.
View Article and Find Full Text PDFImportance: Understanding of SARS-CoV-2 infection in US children has been limited by the lack of large, multicenter studies with granular data.
Objective: To examine the characteristics, changes over time, outcomes, and severity risk factors of children with SARS-CoV-2 within the National COVID Cohort Collaborative (N3C).
Design, Setting, And Participants: A prospective cohort study of encounters with end dates before September 24, 2021, was conducted at 56 N3C facilities throughout the US.
Objective: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations.
View Article and Find Full Text PDFImportance: SARS-CoV-2.
Objective: To determine the characteristics, changes over time, outcomes, and severity risk factors of SARS-CoV-2 affected children within the National COVID Cohort Collaborative (N3C).
Design: Prospective cohort study of patient encounters with end dates before May 27th, 2021.
Article Synopsis
- - The National COVID Cohort Collaborative (N3C) is a massive electronic health record database that provides valuable insights into COVID-19, supporting the development of better diagnostic tools and clinical practices.
- - This study analyzed data from nearly 2 million adults across 34 medical centers to evaluate the severity of COVID-19 and its risk factors over time, using advanced machine learning techniques to predict severe outcomes.
- - Among the 174,568 adults infected with SARS-CoV-2, a significant portion experienced severe illness, highlighting the need for continuous monitoring and adjustment of treatment approaches based on demographic characteristics and disease severity.
Article Synopsis
- The National COVID Cohort Collaborative (N3C) is the largest U.S. COVID-19 patient database, created to provide a comprehensive analysis of clinical characteristics, disease progression, and treatment outcomes across multiple health centers, enhancing predictive and diagnostic tools for COVID-19.
- A study involving over 1.9 million patients from 34 medical centers found significant clinical data, showing that certain factors like age, sex, and underlying conditions affect disease severity, with a notable decrease in mortality rates among hospitalized patients over time.
- The N3C dataset was utilized in machine learning models to successfully predict severe outcomes in COVID-19 patients, achieving high accuracy rates and demonstrating the potential of using electronic health
From EHRs to Linked Data: representing and mining encounter data for clinical expertise evaluation.
AMIA Jt Summits Transl Sci Proc
December 2013
Translational science, today, involves multidisciplinary teams of scientists rather than single scientists. Teams facilitate biologically meaningful and clinically consequential breakthroughs. There are a myriad of sources of data about investigators, physicians, research resources, clinical encounters, and expertise to promote team interaction; however, much of this information is not connected and is left siloed.
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