Fluid flow pathways along the Glarus overthrust derived from stable and Sr-isotope patterns
Article Quality & Performance Metrics
Readership in this journal
EmergingRanked #3,481 of 8,486 articles by views in american journal of science
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 8,486 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
Abstract
Abstract
The National COVID-19 Chest Imaging Database (NCCID) is a centralised database containing chest X-rays, chest Computed Tomography (CT) scans and cardiac Magnetic Resonance Images (MRI) from patients across the UK, jointly established by NHSX, the British Society of Thoracic Imaging (BSTI), Royal Surrey NHS Foundation Trust (RSNFT) and Faculty. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and development of machine learning (ML) technologies that will improve care for patients hospitalised with a severe COVID-19 infection. The NCCID is now accumulating data from 20 NHS Trusts and Health Boards across England and Wales, with a total contribution of approximately 25,000 imaging studies in the training set (at time of writing) and is actively being used as a research tool by several organisations. This paper introduces the training dataset, including a snapshot analysis performed by NHSX covering: the completeness of clinical data, the availability of image data for the various use-cases (diagnosis, prognosis and longitudinal risk) and potential model confounders within the imaging data. The aim is to inform both existing and potential data users of the NCCID's suitability for developing diagnostic/prognostic models. In addition, a cohort analysis was performed to measure the representativeness of the NCCID to the wider COVID-19 affected population. Three major aspects were included: geographic, demographic and temporal coverage, revealing good alignment in some categories, e.g., sex and identifying areas for improvements to data collection methods, particularly with respect to geographic coverage. All analyses and discussions are focused on the implications for building ML tools that will generalise well to the clinical use cases.| Reference Key |
openalex_W2031374246
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | |
| Journal | american journal of science |
| Year | 2002 |
| DOI |
10.2475/ajs.302.6.517
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
ZincirX productsfrom the team behind Journament
-
PatentYazar
Patent drafts for the EPO, USPTO and TürkPatent
From inventor notes to a DOCX draft: description, claims, abstract. The machine drafts, the attorney signs.
See how it works (opens in a new tab) -
Projenator
Have a project idea? Score and plan it with AI
AI scores your idea in five dimensions and builds a phased plan. Your first project is free, no card needed.
Start your first project (opens in a new tab)
Cookies
We use strictly necessary cookies to run the site and keep you signed in. With your permission we would also use Google Analytics to see how the site is used, and Google AdSense to show ads on journal and article pages. Both stay off unless you accept, and you can change your mind at any time. How we use cookies · KVKK notice (Türkiye)
Cookie settings
Comments
No comments yet. Be the first to comment on this article.