Evidence map›Paper›PMID 38173908›Full record

ArticleInformatics in medicine unlocked2024

Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records.

Ebraham Alskaf, Simon M Frey, Cian M Scannell, Avan Suinesiaputra, Dijana Vilic, Vlad Dinu, Pier Giorgio Masci, Divaka Perera, Alistair Young, Amedeo Chiribiri

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Informatics in medicine unlocked, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

10 authors.

Ebraham AlskafSchool of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Simon M FreySchool of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Cian M ScannellSchool of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Avan SuinesiaputraSchool of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Dijana VilicKing's College London, United Kingdom.
Vlad DinuKing's College London, United Kingdom.
Pier Giorgio MasciSchool of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Divaka PereraSchool of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Alistair YoungSchool of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Amedeo ChiribiriSchool of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.

Funding

The Cardiac Atlas ProjectR01HL121754 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MCCULLOCH, ANDREW D., OMENS, JEFFREY H. · 2014 to 2023
$5.0M
NHLBI NIH HHS R01 HL121754Wellcome Trust 203148
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Cardiac magnetic resonanceCoronary artery diseaseElectronic health recordsMachine learningNatural language processingOutcome prediction

Identifiers

PMID38173908
PMCPMC7615463

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.