Zerrendatu honen arabera: egilea "Hernandez, Mikel"
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Comparative assessment of synthetic time series generation approaches in healthcare: leveraging patient metadata for accurate data synthesis
Isasa Reinoso, Imanol; Alberdi Aramendi, Ane (Springer Nature, 2024)Background Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with ... -
Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain
Alberdi Aramendi, Ane; Larrea Lizartza, Xabat (MDPI, 2022)To date, the use of synthetic data generation techniques in the health and wellbeing domain has been mainly limited to research activities. Although several open source and commercial packages have been released, they have ... -
Synthetic Subject Generation with Coupled Coherent Time Series Data †
Larrea Lizartza, Xabat; Alberdi Aramendi, Ane (MDPI, 2022)A large amount of health and well-being data is collected daily, but little of it reaches its research potential because personal data privacy needs to be protected as an individual’s right, as reflected in the data ... -
Synthetic Tabular Data Evaluation in the Health Domain Covering Resemblance, Utility, and Privacy Dimensions
Alberdi Aramendi, Ane (Thieme, 2023)Background. Synthetic tabular data generation is a potentially valuable technology with great promise for data augmentation and privacy preservation. However, prior to adoption, an empirical assessment of generated synthetic ...
