JAMIA is AMIA's premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA's articles describe innovative informatics research and systems that help to advance biomedical science and promote health. Case reports, perspectives, and reviews also help readers stay connected with the most important informatics developments in implementation, policy, and education.
Suzanne Bakken is the Editor-in-Chief and leads a team of informatics leaders serving as the JAMIA Editorial Board.
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JAMIA is indexed in Index Medicus, MEDLINE, EMBASE/Excerpta Medica, CINAHL, Science Citation Index, Social Sciences Citation Index (SSCI), SciSearch, Social SciSearch, Research Alert, Current Contents/Social & Behavioral Sciences, and Current Contents/Clinical Medicine.
Recent JAMIA Articles
Please note: To access the full content of the articles listed below, AMIA members must access JAMIA via the Journal Access Center.
Deep sequential neural network models improve stratification of suicide attempt risk among US veterans.
September 28, 2023
To apply deep neural networks (DNNs) to longitudinal EHR data in order to predict suicide attempt risk among veterans. Local explainability techniques were used to provide explanations for each prediction with the goal of ultimately improving outreach and intervention efforts.
Selective prediction for extracting unstructured clinical data.
September 28, 2023
While there are currently approaches to handle unstructured clinical data, such as manual abstraction and structured proxy variables, these methods may be time-consuming, not scalable, and imprecise. This article aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve the […]
Clustering rare diseases within an ontology-enriched knowledge graph.
September 27, 2023
Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing. Toward that aim, we utilized an integrative knowledge graph to construct clusters of rare diseases.