Publications by authors named "Benjamin Amor"
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.
Introduction: Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants.
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- The study aimed to standardize and fill in missing units from electronic health records (EHRs) by developing a systematic method for converting and validating these measurements, focusing on COVID-19 research.
- The researchers worked with over 3.1 billion patient records and 19,000 unique measurements, successfully harmonizing 88.1% of values and imputing units for 78.2% of records that initially lacked them.
- This new approach enhances the ability to analyze diverse EHR data, making valuable information accessible for public health insights and research efforts.
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.
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- - 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
Objective: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization.
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