AIMC Topic: Privacy

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Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias.

North Carolina medical journal
Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, an...

[Digital environment and population health].

Revue medicale suisse
Health and risk of disease are determined by exposure to the physical, socio-economic, and political environment and to this has been added exposure to the digital environment. Our increasingly digital lives have major implications for people's healt...

Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning.

Mathematical biosciences and engineering : MBE
In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a b...

Federated Multiple Imputation for Variables that Are Missing Not At Random in Distributed Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Large electronic health records (EHR) have been widely implemented and are available for research activities. The magnitude of such databases often requires storage and computing infrastructure that are distributed at different sites. Restrictions on...

A Hybrid AI-Based Method for ICD Classification of Medical Documents.

Studies in health technology and informatics
Automatic document classification is a common problem that has successfully been addressed with machine learning methods. However, these methods require extensive training data, which is not always readily available. Additionally, in privacy-sensitiv...