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Communications Medicine (CM)

Publisher :

Springer Nature

Peer reviewed only icon
Peer reviewed only
Open access journal icon
Open Access
  • oncology
  • cardiology
  • neurology
e-ISSN :

2730-664X

Issue Frequency :

Monthly

Est. Year :

2025

Mobile :

442078334000

DOI :

YES

Country :

United Kingdom

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

commsmed@nature.com, commsproduction@springernature.com, press@nature.com

Journal Descriptions

Communications Medicine is a selective open access journal from Nature Portfolio publishing high-quality research, reviews and commentary across all clinical, translational, and public health research fields. The journal aims to foster collaboration across these different communities to facilitate discovery that will promote health and improve patients’ lives globally. Primary research papers published by the journal represent significant advances in preventing, diagnosing, or treating human disease. Communications Medicine is supportive of Springer Nature’s effort to advance progress towards the UN Sustainable Development Goals (SDGs), in particular SDG3: Good health and wellbeing and SDG5: Gender equality. We welcome submissions that are relevant to the goals of ensuring healthy lives and promoting well-being for all at all ages, as well as of promoting gender equality in health, medical practice, and research.


Communications Medicine (CM) is :

International, Peer-Reviewed, Open Access, Refereed, oncology, cardiology, neurology, global public health, medical research , Online Monthly Journal

UGC Approved, ISSN Approved: P-ISSN , E-ISSN - 2730-664X, Established in - 2025, Impact Factor

Provide Crossref DOI

Indexed in Scopus, WoS, DOAJ, PubMed

Not indexed in UGC CARE

Indexing

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Google Scholar
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Scopus
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WoS

Publications of CM

  • dott image October, 2023

The impact of imputation quality on machine learning classifiers for datasets with missing values

Background Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these mis...

  • dott image July, 2023

Identifying healthy individuals with Alzheimer’s disease neuroimaging phenotypes in the UK Biobank

Background Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer’s disease (AD) in particular, to identify populatio...

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