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Article Abstract

In multicentric studies, data sharing between institutions might negatively impact patient privacy or data security. An alternative is federated analysis by secure multiparty computation. This pilot study demonstrates an architecture and implementation addressing both technical challenges and legal difficulties in the particularly demanding setting of clinical research on cancer patients within the strict European regulation on patient privacy and data protection: 24 patients from LMU University Hospital in Munich, Germany, and 24 patients from Policlinico Universitario Fondazione Agostino Gemelli, Rome, Italy, were treated for adrenal gland metastasis with typically 40 Gy in 3 or 5 fractions of online-adaptive radiotherapy guided by real-time MR. High local control (21% complete remission, 27% partial remission, 40% stable disease) and low toxicity (73% reporting no toxicity) were observed. Median overall survival was 19 months. Federated analysis was found to improve clinical science through privacy-friendly evaluation of patient data in the European health data space.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471812PMC
http://dx.doi.org/10.1038/s41746-024-01293-4DOI Listing

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Article Synopsis
  • Multicentric studies face challenges with patient privacy and data security, prompting an exploration of federated analysis through secure multiparty computation.
  • A pilot study involving 48 cancer patients demonstrated a successful architecture for secure data analysis that complies with stringent European regulations on patient privacy.
  • The treatment led to high local control rates and low toxicity, with a median overall survival of 19 months, showcasing the benefits of privacy-friendly evaluation in clinical research.
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Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations.

Sensors (Basel)

April 2024

Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA.

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g.

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Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework.

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Unlabelled: An increasing number of mental health services are now offered through mobile health (mHealth) systems, such as in mobile applications (apps). Although there is an unprecedented growth in the adoption of mental health services, partly due to the COVID-19 pandemic, concerns about data privacy risks due to security breaches are also increasing. Whilst some studies have analyzed mHealth apps from different angles, including security, there is relatively little evidence for data privacy issues that may exist in mHealth apps used for mental health services, whose recipients can be particularly vulnerable.

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Medical Cyber-Physical Systems support the mobility of electronic health records data for clinical research to accelerate new scientific discoveries. Artificial Intelligence improves medical informatics, but current centralized data training and insecure data storage management techniques expose private medical data to unauthorized foreign entities. In this paper, a Federated Learning-based Electronic Health Record sharing scheme is proposed for Medical Informatics to preserve patient data privacy.

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