Evidence map›Paper›PMID 32477659›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020

Sharing is Caring: Exploring machine learning-enabled methods for regional medical imaging exchange using procedure metadata.

Salaar Liaqat, Joanna Pineda, Jeevaa Velayutham, Allen Lee, Joshua Reicher, Jason Nagels, Marzyeh Ghassemi, Benjamin Fine

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Salaar LiaqatUniversity of Toronto, Toronto, Ontario Canada.
Joanna PinedaUniversity of Toronto, Toronto, Ontario Canada.
Jeevaa VelayuthamUniversity of Toronto, Toronto, Ontario Canada.
Allen LeeUniversity of Toronto, Toronto, Ontario Canada.
Joshua ReicherStanford Health Care, Stanford, California, USA.
Jason NagelsHospital Diagnostic Imaging Repository Services, Toronto, Ontario, Canada.
Marzyeh GhassemiUniversity of Toronto, Toronto, Ontario Canada.
Benjamin FineUniversity of Toronto, Toronto, Ontario Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Seamless sharing between imaging facilities of medical images obtained on the same patient is crucial in providing accurate and efficient care to patients. However, the terminology used to describe semantically similar examinations can vary widely between facilities. Current practice is manual table-based mapping to a standard terminology, which has substantial potential for mislabelled and missing examinations. In this work, we establish several baseline methods for automating the mapping of radiology imaging procedure descriptions to a SNOMED CT based standard terminology. Our best performing baseline, consisting of a bag of words representation and shallow neural network, achieved 96.3% accuracy. In addition, we explore an unsupervised clustering method that explores relevancy matching without the need for an intervening standard. Lastly, we make the procedure name dataset used in this work available to encourage extension of this application.

Identifiers

PMID32477659
PMCPMC7233030

What Socratic holds

Textmetadata
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 Socratic graph.