Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes

Hejblum, Boris P.; Weber, Griffin M; Liao, Katherine P.; Palmer, Nathan; Churchill, Susanne; Shadick, Nancy A.; Szolovits, Peter; Murphy, Shawn N.; Kohane, Isaac S.; Cai, Tianxi

doi:10.1038/sdata.2018.298

Cited by 29 publications

(26 citation statements)

References 28 publications

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“…Visualization‐based interfaces can play a key role in helping data owners, subjects, custodians, and consumers dynamically evaluate the disclosure risks of shared data. For data owners or custodians, visual interfaces [GHK*16, CRVFS15] can help communicate privacy risks by suggesting non‐obvious, probabilistic linkages [HWL*19], let them dynamically evaluate the trade‐offs among data utility and privacy risks [ADSZ*19] by visualizing privacy outcomes from new and evolving metrics [JSH*17], and make more confident decisions regarding data sharing [BVM*17].…”

Section: Gaps and Research Opportunitiesmentioning

confidence: 99%

Privacy‐Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities

Bhattacharjee

Chen

Dasgupta

2020

Computer Graphics Forum

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Preservation of data privacy and protection of sensitive information from potential adversaries constitute a key socio‐technical challenge in the modern era of ubiquitous digital transformation. Addressing this challenge needs analysis of multiple factors: algorithmic choices for balancing privacy and loss of utility, potential attack scenarios that can be undertaken by adversaries, implications for data owners, data subjects, and data sharing policies, and access control mechanisms that need to be built into interactive data interfaces. Visualization has a key role to play as part of the solution space, both as a medium of privacy‐aware information communication and also as a tool for understanding the link between privacy parameters and data sharing policies. The field of privacy‐preserving data visualization has witnessed progress along many of these dimensions. In this state‐of‐the‐art report, our goal is to provide a systematic analysis of the approaches, methods, and techniques used for handling data privacy in visualization. We also reflect on the road‐map ahead by analyzing the gaps and research opportunities for solving some of the pressing socio‐technical challenges involving data privacy with the help of visualization.

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Section: Gaps and Research Opportunitiesmentioning

confidence: 99%

Privacy‐Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities

Bhattacharjee

Chen

Dasgupta

2020

Computer Graphics Forum

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“…A particularly interesting method has been advanced by Hejblum et al, in Boston 85 . It is clear that sufficient clinical details (such as diagnoses with encounter dates) may be enough to identify a subject uniquely, or very nearly so, in two coherent data sets.…”

Section: Resultsmentioning

confidence: 99%

Review of Clinical Research Informatics

Solomonides

2020

Yearb Med Inform

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Objectives: Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports—and sometimes initiates—and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension. Methods: Beyond personal awareness of a range of work commensurate with the author’s own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns (“artificial intelligence”, “data models”, “analytics”, etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered. Results: The substantive sections of the paper—Artificial Intelligence, Machine Learning, and “Big Data” Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence—provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer’s interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work. Conclusions: CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.

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“…Similar efforts have been employed to create virtual patients with specific measurements (i.e. glucose measurements) using mathematical models [24] or using very specific features [25] (non-demographics). The methodology proposed for our virtual population creation has similarities with the concept of Synthea, however, in our approach, a computational model to create additional virtual geometries has been also incorporated in our methodology.…”

Section: Discussionmentioning

confidence: 99%

A Novel Approach to Generate a Virtual Population of Human Coronary Arteries for In Silico Clinical Trials of Stent Design

Pleouras

Sakellarios

Rigas

et al. 2021

IEEE Open J. Eng. Med. Biol.

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A real cardiovascular disease population was utilized to generate virtual patients with cardiovascular disease. To this purpose, data augmentation was performed to create virtual clinical data. Additionally, the imaging of the real population was utilized for 3D arterial reconstruction, which subsequently were used for atherosclerotic plaque growth simulation.Using this model, new arterial geometries were generated. At the final stage the virtual clinical data were combined with the virtual arterial geometries to produce a complete virtual population of atherosclerotic patients.

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Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes

Cited by 29 publications

References 28 publications

Privacy‐Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities

Privacy‐Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities

Review of Clinical Research Informatics

A Novel Approach to Generate a Virtual Population of Human Coronary Arteries for In Silico Clinical Trials of Stent Design

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