ArticleGigaScience2020
An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Clinically reported covert cerebrovascular disease and risk of neurological disease: a whole-population cohort of 367 988 people using natural language processing.Journal of neurology, neurosurgery, and psychiatry · 2026Article
- Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.ESC heart failure · 2024Article
- Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data.Insights into imaging · 2024Article
- Novel linkage approach to join community-acquired and national data.BMC medical research methodology · 2024Article
- The Scottish Medical Imaging Archive: A Unique Resource for Imaging-related Research.Radiology. Artificial intelligence · 2024Article
- The Scottish Medical Imaging Archive: 57.3 Million Radiology Studies Linked to Their Medical Records.Radiology. Artificial intelligence · 2024Article
- Understanding Views Around the Creation of a Consented, Donated Databank of Clinical Free Text to Develop and Train Natural Language Processing Models for Research: Focus Group Interviews With Stakeholders.JMIR medical informatics · 2023Article
- The Utility of Oncology Information Systems for Prognostic Modelling in Head and Neck Cancer.Journal of medical systems · 2023Article
- Next-Generation Capabilities in Trusted Research Environments: Interview Study.Journal of medical Internet research · 2022Article
- A National Network of Safe Havens: Scottish Perspective.Journal of medical Internet research · 2022Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors.
Funding
Abstract
aimTo enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010.
methodsScotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment.
resultsAn extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects.
conclusionsThe data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.