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Real-Time Anonymization and Data Minimization of Vital Signs for Clinical Decision Support Systems and Analytics
Elias Grünewald1, Louis Loechel2, Felix Balzer1
1Charité - Universitätsmedizin Berlin, Institute of Medical Informatics, Germany.
Abstract:
We present a scalable infrastructure based on an implementation of the CASTLE algorithm within state-of-the-art stream processing frameworks to anonymize vital signs from high-frequency patient monitoring systems, e.g., for use in developing Clinical Decision Support Systems. The evaluation shows low latency and preserved clinical accuracy, enabling privacy-compliant system development, clinical trials, and continuous analytics.
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