JMIR Medical Informatics (JMI, ISSN 2291-9694, Journal Impact Factor™ 3.1 (Journal Citation Reports™ from Clarivate, 2023)) (Editor-in-chief: Christian Lovis, MD, MPH, FACMI) is an open-access PubMed/SCIE-indexed journal that focuses on the challenges and impacts of clinical informatics, digitalization of care processes, clinical and health data pipelines from acquisition to reuse, including semantics, natural language processing, natural interactions, meaningful analytics and decision support, electronic health records, infrastructures, implementation, and evaluation.

JMIR Medical Informatics disseminates high-quality research and aims to influence the implementation of informatics solutions to improve health care and care systems. Published by JMIR Publications, the journal focuses on applied, translational research, and implementation science with a broad readership, including clinicians, CIOs, engineers, industry, health informatics professionals, patients, and policy-makers. Submissions involving innovative and practical research on data shareability, reusability, and interoperability are encouraged.

JMIR Publications recently launched JMIR Artificial Intelligence (JAI). AI-centered papers with a core focus on applications of AI (see focus and scope) should preferably be submitted to this new journal.

JMIR Medical Informatics adheres to rigorous quality standards, involving a rapid and thorough peer-review process, professional copyediting, and professional production of PDF, XHTML, and XML proofs. The journal is indexed in MEDLINE, PubMed, PubMed Central, DOAJ, Scopus, and SCIE (Clarivate).

With a CiteScore of 7.9, JMIR Medical Informatics ranks in the 78th percentile (#30 of 138) and the 77th percentile (#14 of 59) as a Q1 journal in the fields of Health Informatics and Health Information Management, according to Scopus data.

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Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation

Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation