For over a decade, Scotland has implemented and operationalized a system of Safe Havens, which provides secure analytics platforms for researchers to access linked, deidentified electronic health records (EHRs) while managing the risk of unauthorized reidentification. In this paper, a perspective is provided on the state-of-the-art Scottish Safe Haven network, including its evolution, to define the key activities required to scale the Scottish Safe Haven network’s capability to facilitate research and health care improvement initiatives. A set of processes related to EHR data and their delivery in Scotland have been discussed. An interview with each Safe Haven was conducted to understand their services in detail, as well as their commonalities. The results show how Safe Havens in Scotland have protected privacy while facilitating the reuse of the EHR data. This study provides a common definition of a Safe Haven and promotes a consistent understanding among the Scottish Safe Haven network and the clinical and academic research community. We conclude by identifying areas where efficiencies across the network can be made to meet the needs of population-level studies at scale.
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance since commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malicious leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. We then propose a taxonomy across involuntary and malicious leakage, followed by description of currently available defences, assessment metrics, and applications. We conclude with outstanding challenges and open questions, outlining some promising directions for future research.
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malevolent leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. We then propose a taxonomy across involuntary and malevolent leakage, available defences, followed by the currently available assessment metrics and applications. We conclude with outstanding challenges and open questions, outlining some promising directions for future research.
Nonalcoholic fatty liver disease (NAFLD) is the commonest cause of chronic liver disease worldwide and a growing healthcare burden. The pathobiology of NAFLD is complex, disease progression is variable and unpredictable, and there are no qualified prognostic biomarkers or licensed pharmacotherapies that can improve clinical outcomes; it represents an unmet precision medicine challenge. We established a retrospective multicentre national cohort of 940 patients, across the complete NAFLD spectrum, integrating quantitative digital pathology, hepatic RNA-sequencing and 5.67 million days of longitudinal electronic health record follow-up into a secure, searchable, open resource (SteatoSITE) to inform rational biomarker and drug development and facilitate personalised medicine approaches for NAFLD. A complementary web-based gene browser was also developed. Here, our initial analysis uncovers disease stage-specific gene expression signatures, pathogenic hepatic cell subpopulations and master regulator networks associated with disease progression in NAFLD. Additionally, we construct novel transcriptional risk prediction tools for the development of future hepatic decompensation events.
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