Evidence map›Paper›PMID 36231175›Full record

ReviewInternational journal of environmental research and public health2022

Privacy-by-Design Environments for Large-Scale Health Research and Federated Learning from Data.

Peng Zhang, Maged N Kamel Boulos

Abstract readReview
In one paragraph

Review in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. From adolescence to Alzheimer's: a neurodevelopmental lifespan model of cognitive vulnerability.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    Review
  2. Article
  3. Review
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  5. Article
  6. Review
  7. Observational
  8. Article
  9. Journal of integrative bioinformatics · 2024
    Article
  10. Article
  11. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Peng ZhangData Science Institute & Department of Computer Science, Vanderbilt University, Nashville, TN 37240, USA.
Maged N Kamel BoulosFaculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal.ORCID 0000-0003-2400-6303

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article offers a brief overview of 'privacy-by-design (or data-protection-by-design) research environments', namely Trusted Research Environments (TREs, most commonly used in the United Kingdom) and Personal Health Trains (PHTs, most commonly used in mainland Europe). These secure environments are designed to enable the safe analysis of multiple, linked (and often big) data sources, including sensitive personal data and data owned by, and distributed across, different institutions. They take data protection and privacy requirements into account from the very start (conception phase, during system design) rather than as an afterthought or 'patch' implemented at a later stage on top of an existing environment. TREs and PHTs are becoming increasingly important for conducting large-scale privacy-preserving health research and for enabling federated learning and discoveries from big healthcare datasets. The paper also presents select examples of successful TRE and PHT implementations and of large-scale studies that used them.

Indexed as

Computer SecurityPrivacyDelivery of Health CareEuropeInformation Storage and Retrievalpersonal health trainsprivacy by designtrusted research environments

Identifiers

PMID36231175
PMCPMC9565554

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.