Publications by authors named "Mark M Bissell"
Article Synopsis
- A study investigated the prevalence of vestibular disorders in patients with COVID-19 compared to those without the virus using data from the National COVID Cohort Collaborative database.
- Results showed that individuals with COVID-19 were significantly more likely to experience vestibular disorders, with the highest risk associated with the omicron 23A variant (OR of 8.80).
- The findings underscore the need for further research on the long-term effects of vestibular disorders in COVID-19 patients and implications for patient counseling.
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 PDFArticle 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