Evidence map›Paper›PMID 38180836›Full record

ArticleSTAR protocols2024

Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis.

Bruno Casella, Walter Riviera, Marco Aldinucci, Gloria Menegaz

Open access · goldAbstract read
In one paragraph

Article in STAR protocols, 2024. 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, top 100% of its field
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, 0 citations in OpenAlex.

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

4 authors at 2 institutions in 1 country.

Bruno CasellaComputer Science Department, University of Turin, 10149 Turin, Italy. Electronic address: bruno.casella@unito.it.
Walter RivieraComputer Science Department, University of Verona, 37134 Verona, Italy.
Marco AldinucciComputer Science Department, University of Turin, 10149 Turin, Italy.
Gloria MenegazEngineering for Innovation Medicine Department, University of Verona, 37134 Verona, Italy.
University of Turin · ITUniversity of Verona · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Federated learning is a cooperative learning approach that has emerged as an effective way to address privacy concerns. Here, we present a protocol for training MERGE: a federated multi-input neural network (NN) for COVID-19 prognosis. We describe steps for collecting and preprocessing datasets. We then detail the process of training a multi-input NN. This protocol can be adapted for use with datasets containing both image- and table-based input sources. For complete details on the use and execution of this protocol, please refer to Casella et al.

Indexed as

COVID-19HumansLearningNeural Networks, ComputerBioinformaticsClinical ProtocolComputer sciencesHealth Sciences

Identifiers

PMID38180836
PMCPMC10801336
OpenAlexW4390597915

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.