Prediction of Budd-Chiari syndrome based on attention mechanisms of high-risk factors in multi-hop graph learning

Lei Wang; Shengli Li; Xiaorui Su; Zhengwei Li; Mengmeng Wei; Bowei Zhao; Maoheng Zu; Qingqiao Zhang; Zhuhong You

doi:10.1007/s11432-024-4358-3

Prediction of Budd-Chiari syndrome based on attention mechanisms of high-risk factors in multi-hop graph learning

Lei Wang
, Shengli Li
, Xiaorui Su
, Zhengwei Li
, Mengmeng Wei
, Bowei Zhao
, Maoheng Zu
, Qingqiao Zhang
, Zhuhong You

Research output: Contribution to journal › Letter › peer-review

4 Scopus citations

Original language	English
Article number	179102
Journal	Science China Information Sciences
Volume	68
Issue number	7
DOIs	https://doi.org/10.1007/s11432-024-4358-3
State	Published - Jul 2025
Externally published	Yes

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10.1007/s11432-024-4358-3

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@article{b42d9c34517f44db8a64666b0349eb0c,

title = "Prediction of Budd-Chiari syndrome based on attention mechanisms of high-risk factors in multi-hop graph learning",

author = "Lei Wang and Shengli Li and Xiaorui Su and Zhengwei Li and Mengmeng Wei and Bowei Zhao and Maoheng Zu and Qingqiao Zhang and Zhuhong You",

year = "2025",

month = jul,

doi = "10.1007/s11432-024-4358-3",

language = "英语",

volume = "68",

journal = "Science China Information Sciences",

issn = "1674-733X",

publisher = "Science China Press ",

number = "7",

}

TY - JOUR

T1 - Prediction of Budd-Chiari syndrome based on attention mechanisms of high-risk factors in multi-hop graph learning

AU - Wang, Lei

AU - Li, Shengli

AU - Su, Xiaorui

AU - Li, Zhengwei

AU - Wei, Mengmeng

AU - Zhao, Bowei

AU - Zu, Maoheng

AU - Zhang, Qingqiao

AU - You, Zhuhong

PY - 2025/7

Y1 - 2025/7

UR - https://www.scopus.com/pages/publications/105008454756

U2 - 10.1007/s11432-024-4358-3

DO - 10.1007/s11432-024-4358-3

M3 - 快报

AN - SCOPUS:105008454756

SN - 1674-733X

VL - 68

JO - Science China Information Sciences

JF - Science China Information Sciences

IS - 7

M1 - 179102

ER -

Prediction of Budd-Chiari syndrome based on attention mechanisms of high-risk factors in multi-hop graph learning

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