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NPJ Digital Public Health

Title Abbreviation

NPJ Digit Public Health

Electronic ISSN

3091-2784

Therapeutic Area

Artificial Intelligence, Community Medicine, Digital Health, Public Health

Readership

Artificial Intelligence Engineers, Artificial Intelligence Researcher, Public Health Hygienists, Public Health Professionals

Scope

npj Digital Public Health considers research that explores the development, implementation, and application of new digital methods and tools in public health, across community, population, and global health, communicable and non-communicable diseases, and cultural and socioeconomic determinants of health. The journal prioritises research that considers impacts on health equity, includes people from affected communities in the design and undertaking of research, and takes an open approach to the sharing of data and tools. The journal covers a broad range of topics including but not limited to: Methods and tools to support the collection and analysis of novel sources of public health data; Novel machine learning and other artificial intelligence methods evaluated in public health applications; Novel mobile applications, monitors, sensors, software, and wearables for measuring or improving health outcomes in the community; New digital public health interventions delivered within the community, especially for vulnerable or disadvantaged populations; Analysis of the impact of exposure to digital transformations in society including use of artificial intelligence, online environments, social media platforms, and gaming; Novel approaches to data linkage, data sharing, federated learning, and platforms for supporting data sharing and analysis; Ethics, governance, policy, regulation, and security with direct implications for digital and informatics applications in public health. The journal typically does not consider research that is disconnected from validated health or behaviour outcomes such as online content analysis, methods tested only on simulated data or in limited settings without external validation, and applications of standard machine learning methods on health data with no other digital public health focus.

Sponsoring Association(s)

No associations affiliated with this journal

Publisher Name

Nature Publishing Group - UK

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