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The American College of Medical Informatics

Fellows of ACMI

ACMI Fellows are eligible to use the designation FACMI, indicating they are an elected Fellow of ACMI. Deserving informaticians from all countries are eligible for election to fellowship. * Denotes deceased fellow
Fellows of AMIA (FAMIA)

Fellows of AMIA

Please congratulate the following AMIA members who have been inducted into the Fellows of AMIA (FAMIA). In recognition of their accomplishments and achievements in the application of informatics, these members are now authorized to use the designation FAMIA (Fellow of the American Medical...
Podcast

ACIF Go-Live Podcast

'Go Live' is a podcast brought you by the AMIA Clinical Informatics Fellows (ACIF) which discusses the intersection of Medicine and Technology, while bringing you entertaining interviews from experts in the field.
Podcast

For Your Informatics (FYI) Podcast

The For Your Informatics (FYI) Podcast showcases talented women at all career stages to reveal the limitless world of biomedical and health informatics professions, and the unique challenges women face pursuing S.T.E.M. careers.
JAMIA Article

The consensus-based CINEX guideline for reporting clinical information extraction studies.

August 8, 2026

Information extraction (IE) from clinical texts has advanced rapidly with recent advances in natural language processing, particularly the advent of large language models (LLMs). However, inconsistent and incomplete reporting of methodologies limits reproducibility, comparability, and clinical translation. We aimed to develop a consensus-based reporting guideline tailored to clinical IE studies.

Author(s): Reichenpfader, Daniel, Zaghir, Jamil, Cécilia-Joseph, Elsa, Delange, Boris, Shaitarova, Anastassia, Sylvestre, Emmanuelle, Dennstädt, Fabio, Zhang, Boya, Teodoro, Douglas, Meystre, Stéphane, Müller, Henning, Denecke, Kerstin

DOI: 10.1093/jamia/ocag136

JAMIA Article

Explainable temporal machine learning of multimorbidity trajectories after acute myocardial infarction: complementing clinical risk scores with mechanistic phenotypes.

August 6, 2026

This study aimed to identify trajectories of multimorbidity following acute myocardial infarction (AMI), using explainable temporal machine-learning methods, and assess their clinical, prognostic, and biological significance.

Author(s): Onoja, Anthony, Elomaa, Kris, Whetton, Anthony D, Geifman, Nophar

DOI: 10.1093/jamia/ocag135

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