Evidence map›Paper›PMID 39568073›Full record

ArticleWorld journal of emergency surgery : WJES2024

A new technology for medical and surgical data organisation: the WSES-WJES Decentralised Knowledge Graph.

Andrey A Litvin, Sophiya B Rumovskaya, Belinda De Simone, Lucienne Kasongo, Massimo Sartelli, Federico Coccolini, Luca Ansaloni, Ernest E Moore, Walter Biffl, Fausto Catena

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Article in World journal of emergency surgery : WJES, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Andrey A LitvinDepartment of Surgical Diseases 3, Gomel State Medical University, University Clinic, Gomel, Belarus.
Sophiya B RumovskayaKaliningrad Branch, Federal Research Center "Informatics and Management" of the Russian Academy of Sciences (FRC IU RAS), Kaliningrad, Russia.
Belinda De SimoneDepartment of Emergency and General Minimally Invasive Surgery, Infermi Hospital AUSL Romagna, Rimini, Italy. desimone.belinda@gmail.com.
Lucienne KasongoGeneral Hospital of Kinshasa, Kinshasa, Congo. ariesproject2023@gmail.com.
Massimo SartelliDepartment of General Surgery, Macerata Hospital, Macerata, Italy.
Federico CoccoliniDepartment of Emergency and Trauma Surgery, University Hospital of Pisa, Pisa, Italy.
Luca AnsaloniDepartment of General Surgery, University Hospital of Pavia, Pavia, Italy.
Ernest E MooreErnest E Moore Shock Trauma Center at Denver Health, University of Colorado, Denver, CO, USA.
Walter BifflDivision of Trauma/Acute Care Surgery, Scripps Clinic Medical Group, La Jolla, CA, USA.
Fausto CatenaDepartment of Emergency and General Surgery, Level I Trauma Center, Bufalini Hospital, AUSL Romagna, Cesena, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe quality of Big Data analysis in medicine and surgery heavily depends on the methods used for clinical data collection, organization, and storage. The Knowledge Graph (KG) represents knowledge through a semantic model, enhancing connections between diverse and complex information. While it can improve the quality of health data collection, it has limitations that can be addressed by the Decentralized (blockchain-powered) Knowledge Graph (DKG). We report our experience in developing a DKG to organize data and knowledge in the field of emergency surgery. METHODS AND

resultsThe authors leveraged the cyb.ai protocol, a decentralized protocol within the Cosmos network, to develop the Emergency Surgery DKG. They populated the DKG with relevant information using publications from the World Society of Emergency Surgery (WSES) featured in the World Journal of Emergency Surgery (WJES). The result was the Decentralized Knowledge Graph (DKG) for the WSES-WJES bibliography.

conclusionsUtilizing a DKG enables more effective structuring and organization of medical knowledge. This facilitates a deeper understanding of the interrelationships between various aspects of medicine and surgery, ultimately enhancing the diagnosis and treatment of different diseases. The system's design aims to be inclusive and user-friendly, providing access to high-quality surgical knowledge for healthcare providers worldwide, regardless of their technological capabilities or geographical location. As the DKG evolves, ongoing attention to user feedback, regulatory frameworks, and ethical considerations will be critical to its long-term success and global impact in the surgical field.

Indexed as

Big DataBlockchainGeneral SurgeryHumansArtificial intelligenceBlockchain in medicineDecentralized knowledge graphEducationElectronic health recordsEmergency surgerySemantic modelWorld journal of emergency surgeryWorld society of emergency surgery

Identifiers

PMID39568073
PMCPMC11577578

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LicenceCC BY-NC-ND
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Registered trials

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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.