Federated electronic health records for the European Health Data Space.

The European Commission's draft for the European Health Data Space (EHDS) aims to empower citizens to access their personal health data and share it with physicians and other health-care providers. It further defines procedures for the secondary use of electronic health data for research and development. Although this planned legislation is undoubtedly a step in the right direction, implementation approaches could potentially result in centralised data silos that pose data privacy and security risks for individuals. To address this concern, we propose federated personal health data spaces, a novel architecture for storing, managing, and sharing personal electronic health records that puts citizens at the centre-both conceptually and technologically. The proposed architecture puts citizens in control by storing personal health data on a combination of personal devices rather than in centralised data silos. We describe how this federated architecture fits within the EHDS and can enable the same features as centralised systems while protecting the privacy of citizens. We further argue that increased privacy and control do not contradict the use of electronic health data for research and development. Instead, data sovereignty and transparency encourage active participation in studies and data sharing. This combination of privacy-by-design and transparent, privacy-preserving data sharing can enable health-care leaders to break the privacy-exploitation barrier, which currently limits the secondary use of health data in many cases.

Aligning Federated Learning with Existing Trust Structures in Health Care Systems.

Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks (e.g., decentralized personal health records) enable storing data locally at the edge to enhance data sovereignty and resilience to single points of failure. Nonetheless, these systems raise concerns on trust and adoption in medical workflow due to non-alignment to current health care processes and stakeholders' needs. The distributed nature of the data makes it more challenging to train and deploy machine learning models (using traditional methods) at the edge, for instance, for disease prediction. Federated learning (FL) has been proposed as a possible solution to these limitations. However, the P2P PHS architecture challenges current FL solutions because they use centralized engines (or random entities that could pose privacy concerns) for model update aggregation. Consequently, we propose a novel conceptual FL framework, CareNetFL, that is suitable for P2P PHS multi-tier and hybrid architecture and leverages existing trust structures in health care systems to ensure scalability, trust, and security. Entrusted parties (practitioners' nodes) are used in CareNetFL to aggregate local model updates in the network hierarchy for their patients instead of random entities that could actively become malicious. Involving practitioners in their patients' FL model training increases trust and eases access to medical data. The proposed concepts mitigate communication latency and improve FL performance through patient-practitioner clustering, reducing skewed and imbalanced data distributions and system heterogeneity challenges of FL at the edge. The framework also ensures end-to-end security and accountability through leveraging identity-based systems and privacy-preserving techniques that only guarantee security during training.

Recombinant Reporter Phage rTUN1::nLuc Enables Rapid Detection and Real-Time Antibiotic Susceptibility Testing of Klebsiella pneumoniae K64 Strains.

The emergence of multi-drug-resistant Klebsiella pneumoniae (Kp) strains constitutes an enormous threat to global health as multi-drug resistance-associated treatment failure causes high mortality rates in nosocomial infections. Rapid pathogen detection and antibiotic resistance screening are therefore crucial for successful therapy and thus patient survival. Reporter phage-based diagnostics offer a way to speed up pathogen identification and resistance testing as integration of reporter genes into highly specific phages allows real-time detection of phage replication and thus living host cells. Kp-specific phages use the host's capsule, a major virulence factor of Kp, as a receptor for adsorption. To date, 80 different Kp capsule types (K-serotypes) have been described with predominant capsule types varying between different countries and continents. Therefore, reporter phages need to be customized according to the locally prevailing variants. Recently, we described the autographivirus vB_KpP_TUN1 (TUN1), which specifically infects Kp K64 strains, the most predominant capsule type at the military hospital in Tunis (MHT) that is also associated with high mortality rates. In this work, we developed the highly specific recombinant reporter phage rTUN1::nLuc, which produces nanoluciferase (nLuc) upon host infection and thus enables rapid detection of Kp K64 cells in clinical matrices such as blood and urine. At the same time, rTUN1::nLuc allows for rapid antibiotic susceptibility testing and therefore identification of suitable antibiotic treatment in less than 3 h.

Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning-A Study on Polar Bears.

The monitoring of animals under human care is a crucial tool for biologists and zookeepers to keep track of the animals' physical and psychological health. Additionally, it enables the analysis of observed behavioral changes and helps to unravel underlying reasons. Enhancing our understanding of animals ensures and improves ex situ animal welfare as well as in situ conservation. However, traditional observation methods are time- and labor-intensive, as they require experts to observe the animals on-site during long and repeated sessions and manually score their behavior. Therefore, the development of automated observation systems would greatly benefit researchers and practitioners in this domain. We propose an automated framework for basic behavior monitoring of individual animals under human care. Raw video data are processed to continuously determine the position of the individuals within the enclosure. The trajectories describing their travel patterns are presented, along with fundamental analysis, through a graphical user interface (GUI). We evaluate the performance of the framework on captive polar bears (Ursus maritimus). We show that the framework can localize and identify individual polar bears with an F1 score of 86.4%. The localization accuracy of the framework is 19.9±7.6 cm, outperforming current manual observation methods. Furthermore, we provide a bounding-box-labeled dataset of the two polar bears housed in Nuremberg Zoo.