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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JAMIA Open is an open access journal and does not require a login or AMIA membership to read articles.
Recent JAMIA Open Articles
Semantic enrichment of Pomeranian health study data using LOINC and WHO-FIC terminology mapping principles.
April 1, 2025
To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship.
Accurate treatment effect estimation using inverse probability of treatment weighting with deep learning.
April 1, 2025
Observational data have been actively used to estimate treatment effect, driven by the growing availability of electronic health records (EHRs). However, EHRs typically consist of longitudinal records, often introducing time-dependent confounding that hinder the unbiased estimation of treatment effect. Inverse probability of treatment weighting (IPTW) is a widely used propensity […]
Aligning prediction models with clinical information needs: infant sepsis case study.
April 1, 2025
Sepsis recognition among infants in the Neonatal Intensive Care Unit (NICU) is challenging and delays in recognition can result in devastating consequences. Although predictive models may improve sepsis outcomes, clinical adoption has been limited. Our focus was to align model behavior with clinician information needs by developing a machine learning […]
LLM-IE: a python package for biomedical generative information extraction with large language models.
April 1, 2025
Despite the recent adoption of large language models (LLMs) for biomedical information extraction (IE), challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Python package for building complete IE pipelines.