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Confidentiality

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Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers.

Studies in health technology and informatics
Clinical text de-identification enables collaborative research while protecting patient privacy and confidentiality; however, concerns persist about the reduction in the utility of the de-identified text for information extraction and machine learnin...

Fair compute loads enabled by blockchain: sharing models by alternating client and server roles.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risk...

Machine learning and genomics: precision medicine versus patient privacy.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient...

[Artificial intelligence in medicine: limits and obstacles.].

Recenti progressi in medicina
Data scientists and physicians are starting to use artificial intelligence (AI) even in the medical field in order to better understand the relationships among the huge amount of data coming from the great number of sources today available. Through t...

Prevalence Estimation of Protected Health Information in Swedish Clinical Text.

Studies in health technology and informatics
Obscuring protected health information (PHI) in the clinical text of health records facilitates the secondary use of healthcare data in a privacy-preserving manner. Although automatic de-identification of clinical text using machine learning holds mu...