JAMIA Open is a peer-reviewed, online-only, and Gold Open Access journal, JAMIA Open provides a global forum for the publication of novel research and insights in the major areas of informatics for biomedicine and health (e.g., translational bioinformatics, clinical research informatics, clinical informatics, public health informatics, and consumer health informatics), as well as related areas such as data science, qualitative research, and implementation science.
JAMIA Open articles, which include application notes, database notes, and patient/community perspectives, alongside original research, reflect the broad diversity of the field of informatics community, focusing on the intersection of informatics, health, communication, and technology, and how that intersection can support patient care through research, practice, and education. JAMIA Open authors are encouraged to make data and source code accessible through publicly accessible repositories that can be cited using digital object identifiers. Accepted manuscripts are required to have a patient/community facing abstract that highlights key findings. Author guidelines.
Neil Sarkar, PhD, MLIS, FACMI, is the Editor-in-Chief and leads a team of informatics leaders serving as the JAMIA Open Editorial Board and Associate Editors.
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Recent JAMIA Open Articles
JAMIA Open Article
October 1, 2024
The publication of the Phoenix criteria for pediatric sepsis and septic shock initiates a new era in clinical care and research of pediatric sepsis. Tools to consistently and accurately apply the Phoenix criteria to electronic health records (EHRs) is one part of building a robust and internally consistent body of […]
JAMIA Open Article
October 1, 2024
To provide a foundational methodology for differentiating comorbidity patterns in subphenotypes through investigation of a multi-site dementia patient dataset.
JAMIA Open Article
October 1, 2024
Accurately identifying clinical phenotypes from Electronic Health Records (EHRs) provides additional insights into patients' health, especially when such information is unavailable in structured data. This study evaluates the application of OpenAI's Generative Pre-trained Transformer (GPT)-4 model to identify clinical phenotypes from EHR text in non-small cell lung cancer (NSCLC) patients […]
JAMIA Open Article
October 1, 2024
Clinical note section identification helps locate relevant information and could be beneficial for downstream tasks such as named entity recognition. However, the traditional supervised methods suffer from transferability issues. This study proposes a new framework for using large language models (LLMs) for section identification to overcome the limitations.